Trending December 2023 # Top Big Data/Data Science Job Openings In Adobe To Watch Out For This Month # Suggested January 2024 # Top 17 Popular

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Land a career in Adobe with these top big data/data science jobs.

Many businesses encountered turbulence in 2023, yet big data/data science saw substantial demand and growth.

Data science

professionals are in high demand all across the world. These job opportunities will continue to grow after 2023, with over 1.5 lakh more positions being added. This is a natural reaction to data’s importance as a resource for businesses in the digital age. We’ve compiled a list of the top 10

Big Data

/Data Science job openings in Adobe to watch out for this month.

Big Data Developer




5+ years in the design and development of large-scale data-driven systems. 

Work experience with one or more big data technologies such as Apache Spark. 

Work experience with one or more NoSQL storage systems such as Aerospike, HBase, Cassandra. 

Contribution to open source is desirable. 

Great problem solving, coding (in Java/Scala, etc.), and system design skills. 

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Data Scientist


Noida, Uttar Pradesh 


Perform exploratory data analysis quickly, generate and test working hypotheses, and discover new trends and relationships.

Reports and presentations can be used to communicate results and educate others.

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Senior Data Engineer


Bengaluru, Karnataka 


Develop distributed data processing pipelines using Apache Spark. Build and maintain pipelines as needed to power critical business metrics to measure the performance of pages on the website. 

Responsible for crafting, developing sophisticated data applications/pipelines on large-scale data platforms using Apache Spark, Hadoop, Python/Scala. 

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Computer Scientist – Python


Bengaluru, Karnataka 


Developing Java backend services that would make use of and add value to Adobe’s own data platform. 

Building the company’s tracking services in a cookie-less world. 

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Web & Data Science Analyst


Noida, Uttar Pradesh 


Selecting features, building and optimizing classifiers using machine learning techniques. 

Data mining using state-of-the-art methods. 

Doing ad-hoc analysis and communicating results in a clear manner.

Crafting automated anomaly detection systems and constant tracking of its performance.

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Computer Scientist


San Francisco 


Build high-performance and resilient micro-services for event and data processing at scale. 

Design new features and create functional specifications by working with product management and engineering team members. 

Develop software solutions by understanding the company’s customer’s requirements, data flows, and integration architectures. 

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Data Scientist/Senior Product Analyst, Experimentation


San Jose 


You will work with data engineers to design and automate data pipelines to scale experimentation and user analytics. 

In collaboration with a multi-functional team of product management, marketing, and engineering, you will tap into the underlying data, align on metrics/methodologies and generate insights to develop valuable, highly effective programs. 

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Web Analyst & Data Science




Responsible for providing Analytical Insights & Intelligence support aligned towards business or project or initiative. 

Drive partnership with US Web Analytics team, Go-To-Market teams, eCommerce teams, chúng tôi Product Managers team, etc., and be the Subject Matter Expert for aligned areas. 

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Adobe Analytics – Big Data Software Developer




Transform the business requirements into feature specifications.

Contribute to the design and implementation of new features.

Design and implement new features, APIs, unit and integration test suites.

Be involved in all the product development and delivery stages, as part of a unified engineering team.

Data Engineer


San Jose 


Design, develop & tune data products, applications, and integrations on large-scale data platforms (Hadoop, Snowflake, Alteryx, SSIS, Kafka Streaming, Hana, SQL server) with an emphasis on performance, reliability, and scalability, and most of all quality. 

Analyze the business needs, profile large data sets and build custom data models and applications to drive the Adobe business decision making and customers experience.

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Top Ten Promising Big Data Companies To Look Out For In 2023

Big data companies are flourishing across the world with their data visualization features

Big data companies are in huge demand in the global tech market for their effective data management skills with data analytics and data visualization. Data-centric world is running towards different kinds of data to have a deep understanding of consumer behaviour in recent times. Big data helps to provide the utmost customer satisfaction and enhance customer engagement through these big data companies. Let’s explore some of the top ten big data companies in 2023 that are promising to leverage.  

1. Big Panda

BigPanda keeps businesses running with Event Correlation and Automation, powered by AIOps. With BigPanda, IT Ops, NOC, DevOps, and SRE teams prevent outages, lower operational costs and deliver extraordinary customer experiences. BigPanda is well known for reducing costs, improving performance and availability, and accelerating business velocity. BigPanda is ideal for Midsize Enterprises, C-suite, and IT Ops pros. BigPanda enables IT organizations to take costs out of their operations. By boosting efficiency, reducing escalations, slashing downtime, eliminating or shortening bridge calls, flattening headcount, reducing SLA penalties, and consolidating tools, BigPanda customers can reduce operating costs by up to 50%.  

2. BlueCloud Technologies

BlueCloud is a professional services company that serves enterprise customers in EMEA by providing a myriad of world-class services and solutions. BlueCloud provides various services like custom development, solution implementation, outsourcing, technical assessment, CRM solutions, cloud solutions, project management, and quality control. Collaboration, web portals and smart applications, infrastructure solutions, identity management, business process automation, and MiddleWare ESB are the solutions provided by the company.  

3. Clairvoyant 4. Cogito

Cogito’s speech analytics was deployed within population health and care management programs at premier health and insurance companies. Thousands of interactions were analyzed, generating millions of data points to further enhance the effectiveness of Cogito’s behavioural models. Implementing critical and transformative customer service improvement strategies has always been challenging. Cogito professional services are here to help its clients realize value from Cogito solutions by designing and delivering successful implementations and supporting ongoing client success.  

5. Cloudera

Cloudera helps innovative organizations across all industries tackle transformational use cases and exact real-time insights from an ever-increasing amount of data to drive value and competitive differentiation. Cloudera delivers an enterprise data cloud for any data, anywhere, from the Edge to AI. Build, deploy and scale ML and AI applications through a repeatable industrialized approach and turn data into decisions at any scale, anywhere. Cloudera helps clients transform from both a technological and a practical standpoint, speeding up time to results with enterprise AI and ML. With the company’s modern, open platform and enterprise tools, Cloudera enables clients to build and deploy AI solutions at scale, efficiently, and securely, anywhere they want.  

6. Crunchbase

Crunchbase is a platform for finding business information about private and public companies. Crunchbase information includes investments and funding information, founding members and individuals in leadership positions, mergers and acquisitions, news and industry trends. Crunchbase’s best-in-class private company data offers insight into target companies’ teams, funding status, growth trends, tech stack, web traffic, investments, and more to personalize the outreach and increase engagement.  

7. CB Insights

CB Insights’ technology insights platform, intelligence analysts, and global network of executives and start-ups empower people to articulate compelling answers to difficult questions — about growth, the competition, and technology. The company aggregates and analyses massive amounts of data and use machine learning, algorithms, and data visualization to help corporations replace the three Gs (Google searches, gut instinct, and guys with MBAs) so they can answer massive strategic questions using probability, not punditry.  

8. Centerfield

Centerfield develops intelligent big data-driven marketing and sales technology utilizing real-time biddable media (RTB), automated call routing, and customized scripting. Our proprietary platform, Dugout, combined with our 1500-person sales and retention center delivers new customers at scale to many of the leading brands worldwide. Centerfield’s industry-leading platform automates end-to-end customer acquisition for millions of shopping experiences each year. The company accurately attributes every conversion to maximizing efficiency.  

9. CloudTrains Technologies

CloudTrains is a start-up for start-ups, SMEs, and enterprises. The company helps start-ups and businesses to work smart. CloudTrains is a mobile app and web development company with a world-class team of talented data scientists, app and web developers, designers, engineers, creative artists, and brand strategists. Headquartered at Gwalior and development center at Pune and Florida, USA. CloudTrains have more than 6+ years of experience in the IT Industry. The company is a one-stop destination for web and mobile app design and development services.  

10. Comsense Technologies

Top 8 Job Roles In Data Science Industry That You Should Know!


Understand the various job roles and career opportunities in the Data Science industry

Also, know the skills required for the jobs role in the Data Science Industry


The increased use of the internet globally is generating data at an unprecedented pace. With the pandemic hitting us, we are consuming and producing more data than ever. As a result, we see a significant rise in the demand for more people in the data science industry. Consequently, the data science industry has been continuously hiring people for various job roles.

But one of the things that I have observed about these opportunities is the indistinguishable description of job roles. Even though the majority of recruiters use the right description for various data science job roles, the candidate might not be able to differentiate. Therefore, this confusion between the job role and job description might lead the aspirant to apply for the wrong jobs and missing out on the appropriate opportunities.

I personally faced this last year while I was looking for an internship or a job as a Business Analytics Professional but failed to find them. I concluded, either the job role demanded way more than what an analytics professional is required to do. Or it just did not match with what a Business Analytics Professional does. Have you had the same experience?

Even in such a flourishing industry, there is a confusion with respect to job roles. A loose understanding of job roles may cost aspirants their dream job and lif. And this is precisely the driving force behind writing this article. I will be going through the Different Job Roles in the Data Science industry along with the necessary skill set for each job role.

Note: If you are trying to get some resources to kick start your Data Science journey, here is a link to our Free Courses including ebooks

Data Scientist

This is the hottest job role in the industry. Even Harvard could not deny calling Data Scientist the ‘Sexiest Job of the 21st century’. So what does the job of a Data Scientist entail? What does a Data Scientist do?

Firstly, they understand the challenges or problems of the business and discuss it with the stakeholders regarding the same. The next task is to convert the defined business problem into a data science problem and use the analytical and technical skills to better understand and solve the problem. Finally, the data scientists would provide the organization with the best solutions to tackle the underlying problem.

It is important for a data scientist to asks the right questions, processes & cleans data, and perform Exploratory Data Analysis. And he also builds multiple machine learning models based on set objectives with the minimum possible error rate. Also, the Data Scientist works on problems that contribute to the non-linear growth of the company.

PayScale reports the average salary earned by an experienced is approximately Rs.18,27,036 per annum.

Refer to this article to understand how to become a data scientist- Your Ultimate Learning Path to Become a Data Scientist and Machine Learning Expert in 2023

And to start your journey in Data Science, have look at our course – Introduction to Data Science

Data Analyst

Data Analysts take a technical role in developing, implementing, and maintaining analytic systems. He works with project managers to identify critical metrics and KPIs, and deliver actionable insights to relevant decision-makers. Like the data scientist, the analyst also performs statistical analysis and make predictive models.

They need to create and maintain rich interactive visualizations through data interpretation and analysis integrating various reporting components. They coordinate with various departments in the organization to understand their data needs. After understanding the requirements, the analyst evaluates internal systems for efficiency, problems, and inaccuracies, developing and maintaining protocols for handling, processing, and cleaning data.

PayScale reports the average salary earned by an experienced Data Analyst is approximately Rs.8,52,516 per annum.

Business Analyst

The role of a Business Analyst is a slightly different but important role in the data domain. The Business Analyst is responsible to use data to drive business decisions. He serves as the link between the IT department and the management. The analyst has an understanding of all the technical aspects of the data industry. But his business acumen and ability to combine decision making with data makes him an important figure within the organization.

The Business Analyst analyzes numbers to smoothen business processes. He identifies the right target customers and also looks into the effectiveness of the various campaigns. The Business Analyst also uses data to create various business plans.

PayScale reports the average salary earned by an experienced Business Analyst is approximately Rs.11,89,684 per annum.

In case you are unclear about how is this role different, refer to this article – Business Analytics vs. Data Science – Which Path Should you Choose?


The name itself is self-explanatory. The statistician is someone who has a sound understanding of the entire statistics and the underlying methodologies. He is responsible for creating data-driven surveys, opinion polls, and questionnaires. Moreover, he meets with the management to discuss the best methods of collecting data.

A Statistician uses his training in statistics to draw conclusions and take decisions accordingly. In addition, he may also be required to create reports by analyzing and interpreting data for management and for different departments.

PayScale reports the average salary earned by an experienced Statistician is approximately Rs.11,00,000 per annum.

Data and Analytics Manager

The Analytics manager looks into the hiring and training requirements for the data team. In addition, the analytics manager has to maintain a full report of everything happening within his team. Also, he communicates the results obtained and the business impact of insights to the stakeholders.

PayScale reports the average salary earned by an experienced Data and Analytics Manager is approximately Rs.20,24,317 per annum.

Database Administrator

The Database Administrator ensures that the database is accessible to every stakeholder in the organization. He monitors the database and ensures that the database is performing legitimately and that the necessary safety measures are in place to keep the stored data safe. The Database administrator grants or revokes access to information in the database to the employees.

He regularly installs, upgrades, and manages database applications. The administrator also diagnoses and troubleshoots database errors. Also, maintaining database reports, visualizations, and dashboards is a Database Administrator’s job responsibility.

PayScale reports the average salary earned by an experienced Database Administrator is approximately Rs.10,84,793 per annum.

Data Engineer

Data Engineer is responsible for transforming data into an easily analyzable format. They closely work with the Data Science team and are largely in charge of designing solutions for data scientists that enable them to do their jobs. The Data Engineer creates optimal pipeline architecture. They assemble large complex data sets for business needs.

They build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources. The engineer also develops tools to extract useful insights into customer acquisition. Above all, they strive for greater functionality in the data system.

PayScale reports the average salary earned by a mid-career Data Engineer is approximately Rs.12,35,233 per annum.

Data Architect

The role of a Data Architect us to integrate, centralize, protect, and maintain the data sources of an organization. They often work with the latest technologies such as Spark and always need to be on top of the game to stay relevant. Data Architects play an important role in creating a blueprint for the best possible management of the database. They also organize data both at a macro and a micro-level.

In addition to this, data architects should have good database modeling and designing skills. They are the ones who decide the storage and consumption of data within the organization. A data architect maintains the metadata registry.

PayScale reports the average salary earned by an experienced Data Architect is approximately Rs.20,54,167 per annum.


To summarize, in this article, we explored the prominent job roles existing in the Data Science Industry. We show the various languages and skills required for each of thee 8 job roles. I hope it will help you in deciding which job roles to apply for in your future.


11 Superb Data Science Videos Every Data Scientist Must Watch


Presenting 11 data science videos that will enhance and expand your current skillset

We have categorized these videos into three fields – Natural Language Processing (NLP), Generative Models, and Reinforcement Learning

Learn how the concepts in these videos work and build your own data science project!


I love learning and understanding data science concepts through videos. I simply do not have the time to pour through books and pages of text to understand different ideas and topics. Instead, I get a much better overview of concepts via videos and then pick and choose the topics I want to learn more about.

The sheer quality and diversity of topics available on platforms like YouTube never ceases to amaze. I recently learned about the amazing XLNet framework for NLP from a video (which I have mentioned below for your consumption). This helped me grasp the concept so I could explore more about XLNet!

I strongly believe structure is very necessary when we’re learning any concept or topic. I follow that approach each time I write an article as well. That’s why I’ve categorized these videos into their respective domains, primarily Natural Language Processing (NLP), Generative Models and Reinforcement Learning.

So are you ready to dive in and explore the length and breadth of data science through these fascinating videos?

Without any further ado, here are 11 awesome Data Science Videos:

Natural Language Processing (NLP)

XLNet explained

How does Google Duplex work?

Google’s POEMPORTRAITS: Combining Art and AI

Generative Models

Dive into Variational Autoencoders!

Create Facial Animation from Audio

MuseNet Learned to Compose Mozart, Bon Jovi, and More

Reinforcement Learning

Teaching the Computer to drive

Learn how AlphaGo Zero works

Google DeepMind AI learns to walk

AI learns to play 2048


Adobe develops AI to detect Photoshopped Faces

XLNet Explained

XLNet is the hottest framework in NLP right now. You simply must be aware of what it is and how it works if you want to carve out a career in this field. I came across this video recently and wanted to share it with the community as soon as possible.

XLNet is the latest state-of-the-art NLP framework. It has outperformed Google’s BERT on 20 NLP tasks and achieved state-of-the-art results on 18 of them. That is very, very impressive.

Make sure you check out our article covering XLNet and it’s powerful ability here.

The below video provides a clear explanation of the original XLNet research paper. Note: You might need to know a few NLP concepts beforehand to truly grasp the inner workings of XLNet.

How does Google Duplex work?

Remember when Sundar Pichai went on stage and sent the whole world into a frenzy when he unveiled Google Duplex in his keynote at Google I/O 2023? I remember listening in complete awe to the super-realistic calls that the AI made.

It took a bit of time for the data science and NLP community to come up with an explanation as to how Google Duplex actually works. It’s pretty powerful and has the potential to change how we interact with machines.

So the million dollar question – did Google Duplex pass the Turing Test!? You decide after watching this video:

Google’s POEMPORTRAITS: Combining Art and AI

I am an artist and the prospect of combining any art form with Artificial Intelligence is extremely enticing. In a world where there is so much fear around AI, such applications are more than welcome.

Google’s POEMPORTRAITS AI has been trained on nineteenth-century poetry using NLP techniques. You can contribute and donate a word to generate your own POEMPORTRAIT. Check out how this awesome concept works:

Generative Models Dive into Variational Autoencoders!

Here’s one of our favorite reinforcement learning experts Xander Streenbrugge from his wonderful ArxivInsights channel.

Variational Autoencoders (VAEs) are powerful generative models with diverse applications. You can generate human faces or synthesize your own music or use VAEs for removing noise from images.

I like this video a lot. Xander begins with an introduction to basic autoencoders and then goes into VAEs and disentangled beta-VAEs. Quite technical, but explained beautifully and concisely, in typical Xander style.

Xander is coming back to DataHack Summit this year so you can hear from him and meet him in person!

Create Facial Animation from Audio

I was immediately drawn to the video when I read the title. This is Generative Models at their best! You can not only generate facial animation from audio but also generate different emotions for the same audio. And the facial expressions look incredibly natural.

If you aren’t following Two Minute Papers, you’re missing out. They regularly churn out videos breaking down the latest developments in easy-to-understand fashion. It’s a gem of a channel.

MuseNet Learned to Compose Mozart, Bon Jovi, and More

OpenAI’s MuseNet is a deep neural network that generates musical compositions with different instruments and combines different styles. It uses the same general-purpose unsupervised technology as GPT-2 and the results are amazing.

Never heard of GPT-2? It’s an NLP framework on par with XLNet. Check out how MustNet works here:

Reinforcement Learning Teaching a Computer to Drive

Self-driving cars have always fascinated me. The sheer scale of an autonomous vehicle’s project is staggering. There are so many components, both on the hardware side as well as the data science side, that need to align for this project to work.

This is a perfect video for beginners to learn about Genetic Programming and Reinforcement Learning and how they are used to create powerful applications. Simon’s personality kept me hooked until the very end.

And I am definitely trying the project on my own.

Learn how Google DeepMind’s AlphaGo Zero works

Another great video by Xander. He explains Google DeepMind’s popular paper on AlphaGo Zero.

AlphaGo Zero is a new version of the original AlphaGo program that beat human champion Lee Sedol comprehensively. I recommend reading our article on Monte Carlo Tree Search, the algorithm behind AlphaGo before proceeding to learn about AlphaGo Zero.

AlphaGo Zero uses Reinforcement Learning to beat the world’s leading Go players without using data from human games.

“AlphaGo Zero surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0, reached the level of AlphaGo Master in 21 days, and exceeded all the old versions in 40 days.”

Source: Wikipedia

Google DeepMind’s AI learns to Walk

This video is both hilarious and informative. Exactly the type of video I like when I’m learning new things! It was funny to watch the AI learn to walk. But at the same time, it left me marveling at the power of Reinforcement Learning.

The video discusses 3 papers to try and explain how the AI learned to walk and it is surprisingly simple to understand.

AI learns to play 2048

Have you ever played the 2048 game? It is super addictive once you get the hang of it. I used to easily finish games earlier but not anymore. Being a data science enthusiast, I am going to train my computer to play it with the help of this awesome video.

This is another example of the use of Genetic Programming and Evolutionary Algorithms.

BONUS: Adobe develops AI to detect Photoshopped Faces

Adobe is a market leader in image and video manipulation software. Other companies have tried, but not many have even gotten close to Adobe’s level.

Last month, Adobe announced its research efforts to detect manipulated images. High time someone did that! It will soon be impossible to tell real from fake given how quickly GANs have taken over the world.

Imagine Donald Trump challenging Kim Jong Un to a nuclear war and then claiming that it was a deepfake and shrugging off all responsibility! We need to avoid those situations turning into reality. This video shows how Adobe’s algorithm works and tried to combat fake images:

End Notes

I love knowing about the latest research in data science, machine learning and AI. But I find it hard to read papers. It takes a lot of time and effort – something not every data science professional has. I am sure many of you struggle with the same. Consuming videos is the ideal way to get an overview of these concepts.

You can then pick and choose where your interests lie and try to spin up a project or blog post on it. Trust me, it’s a wonderful way to learn and ingrain new data science concepts.


Top 10 Data Science Companies In India To Work For 2023

A rundown of top data science companies for 2023

Data Science is an umbrella term that covers areas – Data Analytics, Big Data, Business Analytics, Machine Learning, Artificial Intelligence and Deep Learning. This immense field has changed what businesses look like into data and convert them into usable insights. Advancement in technologies and data science tools have changed the manners by which organizations work and grow. India, being a mother lode of ability, is the top destination for national and global companies searching for qualified Data Science experts. Over recent years, the demand for Data Scientists has developed exponentially. More than 97,000 data science jobs are open in 2023 just in India. Let’s look at some of the top data science companies that you can consider to land your next job.  

1.  Ugam, a Merkle company

Ugam, a Merkle company and part of Dentsu, is a leading next-generation data and analytics company helping businesses make superior decisions. Ugam’s customer-centric approach blends data, technology, and expertise, enabling impactful and long-tenured relationships with more than 85 Fortune 500 companies. Recognized as one of the best firms for data scientists to work, Ugam’s data scientists get an opportunity to work directly with client business stakeholders on end-to-end projects. Leveraging Ugam’s proprietary analytical tools, frameworks, and AI / ML technology (Ugam’s JARVIS), they deliver superior results across industries like Retail, Hi-Tech, BFSI, Distribution and Manufacturing as well as Market Research and Consulting. Ugam not only offers data scientists an opportunity to accelerate their career but also offers stability and a unique culture. Led by its founding partners and a committed leadership team, it has experienced significant Y-O-Y growth since inception. The company has a high percentage of long tenured (more than 10 years) and boomerang data scientists. Ugamites vouch for Ugam’s people-centric culture, upskilling, and mobility (across geographies or teams) opportunities and positively driven work environment, thereby making it Analytics Insight’s top pick for data scientists to work.  

2.  MuSigma

Recognizing itself as the biggest solution provider of decision science and analytics, MuSigma has its headquarters in Chicago, US. It has workplaces world-over, with Bangalore as its central delivery hub. Your role as a Data Scientist at MuSigma would include analysing data, refining as well as rearranging it and lastly assessing the outcomes. Mu Sigma is one of the favorite places for employees to work in the field of data science because of its open culture. It’s quite well-known as it serves Fortune 500 organizations through decision science and big data analytics. Mu Sigma has a creative and interactive way to invite its new employees through what they call MSU (Mu Sigma University), where the new employees get hands-on training on various challenging projects under the direction of senior experts in the organization.  

3.  Manthan

Another leading data science firm is Manthan! They have an extraordinary methodology with regards to business solutions. They collaborate the power of AI and analytics that gives data-driven insights of a business model. They assist organizations with making educated decisions by means of rigid data analysis and technology. This organization serves various businesses from technology to telecom, just as retail, pharma, and travel. Its data scientists give an analytics model for the decision-making of its customers. At Manthan, data scientists are constantly encouraged to test performance of different data-driven products using leading technologies like AI, ML, etc. that are relative to their domain. They are given the opportunity to manage large amounts of data to find valuable insights that streamline business procedures, identify opportunities using research and management tools, and reduce risks.  

4.  Absolutdata

Absolutdata bestows impressive learning and growth curves than different players in the market. With explicit, role-based learning on niche subjects, data scientists can upskill and concentrate on fortifying core fundamentals. Moreover, they are urged to take up new jobs and responsibilities that draw out the best in them while permitting them to grow into influential positions.  

5.  Fractal Analytics

Founded in 2000, Fractal Analytics has developed as one of the top analytics service providers in the nation. With a worldwide impression bragging about a few Fortune 500 organizations from ventures like retail, insurance and technology, there is unquestionably no halting this one. The organization is at present employing Data Scientists for its workplaces in Bangalore, Mumbai and Gurgaon. Fractal Analytics has built-up a good customer base. Hence, as a data scientist you’ll be working on significant projects such as business analytics, healthcare, and decision-making. With the pool of data scientists working at Fractal increasing, they’ve started providing training and mentorship programs that will enable its employees to enhance their skills. You’ll primarily work on forecasting projects. If data analytics and forecasting is what you wish to do, Fractal Analytics is your place then.  

6.  BRIDGEi2i Analytics

Established in India in May 2011 by Prithvijit Roy (CEO), Pritam K Paul (CTO) and Ashish Sharma (COO) , their asset-based consulting approach covers the entire range, directly from data science to machine learning-centered knowledge improvement to actionability by way of AI accelerators implementation and finally contextualization of the goal to the companies. The company has an attrition rate of 10-12% and believes in sustaining its talent pool with constant, experiential-based learning. It offers its employees chances to progress rapidly into leadership roles. The company has a strong recognition system that guarantees that all the contributions of employees are valued and appropriately awarded.  

7.  Latent View

Latent View furnishes customers with a range of data science services like counseling, Data Architecture and Design, and data implementation and operations. They are upheld by scalable modern architecture. The work culture is friendly and development-oriented. They encourage workers to see each viewpoint in three edges: team, customer, and society. Having Paypal, IBM, Microsoft, and Cisco as the organization’s esteemed customers, it urges data scientists to have a 360-perspective on each project to permit customers to streamline decisions on investment and anticipate the most recent revenue streams as well as to predict product trends. The most compelling motivation they provide and hold individuals is a mix of learning and working with an excellent peer group in addition to a chance to take care of complex business issues with acing analytics abilities in a climate that makes the whole journey rewarding.  

8.  Accenture

Accenture emphasizes that enormous and complex organizations can profit by efficient utilization of their own data. They have to depend on the experts for it – data scientists. If you are passionate about characterizing procedures and delivering on them with the assistance of creative utilization of integrated data then Accenture is the company to be. The unmistakable worldwide professional service provider has openings for data scientists in the field of business process specialization and data management to give some examples. At Accenture, data scientists will also be exposed on the strategy side as well. They’ll be responsible to define strategies and provide solutions using vast amounts of data.  

9.  Genpact

Genpact has more than 1500 data scientists who work as a centralised hub model with customer experience as its main concern. The organization centers around developing the pool of citizen data scientists through different projects, for example, Machine Learning Incubator’ and ‘ML Upgrade’. ML Incubator program is an in-house AI/ML college which aims to upskill more than 600 existing domain experts every year by giving them structured and instructor paced learning structure, in the fields of data engineering, data science.  

10. Tiger Analytics

Tiger Analytics is spearheading what AI and analytics can do to take care of the absolute hardest issues encountered by companies worldwide. They create bespoke solutions fueled by data and technology for a few Fortune 500 organizations. They have workplaces in various urban communities across the US, India, and Singapore, and a substantial remote global workforce.

Google Launches New Structured Data For Job Listings

Google is introducing a new structured data markup property for job listings that allow prospects to apply directly on the employer’s website.

In addition, Google is mandating a new editorial content policy in an effort to users can understand the content in a job listing and easily apply for it either directly or another way.

First let’s look at the new structured data property.

New directApply Markup For Job Listings

The new directApply property allows employers to indicate if there’s an option for prospective employees to apply for a job on their website.

Google says this markup is suitable for job listings that meet a certain set of user actions required to apply for the job. Namely, the user must be offered a short and straightforward application process.

Employers offer a “direct apply experience,” as defined by Google, if one of these conditions are met:

The user completes the application process on your site.

In other words, if the job listing requires applicants to upload a resume and then type all that same information again in an application form, it’s not eligible for this markup.

Site owners can start using this markup right away, though there may not be any immediate effect in search results as Google works to integrate this information into its index.

New Content Policy For Job Listings

Google asks that job listings also follow basic grammar rules, such as proper capitalization.

Based on research findings, Google offers the following tips to employers to improve job seeker trust and potentially attract more applicants:

No scams: Verify that no job listings represent scams or spams. Listings must represent real job opportunities.

Improve user experience: Sites with poor user experience ask for user information when it’s not necessary, have poor quality pages, or have complex application processes.

Remove expired job posts: Don’t leave a job post open if it is no longer accepting new applications.

Genuine dates: Don’t mask old jobs as new ones and don’t update the DatePosted property if there was no change to the job post.

No wrong or misleading info: This includes incorrect salary, location, working hours, employment type, or other job specific details.

Google’s new editorial content policy for job listings will go into effect on October 1, 2023.

Source: Google Search Central Blog

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