Senior Data Scientist|NR-2026-0241 (4)

Media.net; Mumbai; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Media.net is seeking a senior data science professional to build ML/IR models and NLP-based solutions at scale. You will turn business requirements into prototype systems, collaborating with engineers to measure model performance and drive impact on publisher revenue and advertiser value.

The role requires deep ML knowledge, strong Python programming, and experience with Spark. You will contribute to a research agenda and translate complex problems into practical, scalable algorithms across

Qualifications

• PhD/BS/MS in CS, Statistics, AI, Machine Learning or related field.
• 6+ years of experience building ML/AI/Information Retrieval models.
• Extensive knowledge in ML, data mining, AI, statistics.
• Understanding of supervised and unsupervised algorithms including linear models, trees, forests, boosting.
• Excellent analytical and problem-solving abilities.
• Good knowledge of Python programming.
• Experience with Apache Spark is desirable.

Responsibilities

• Analyze business requirements and extract relevant information from large historical data.
• Use information retrieval, NLP and ML to build prototype solutions with scale, speed and accuracy.
• Collaborate with engineering teams to implement prototypes and define performance metrics.
• Develop research agenda and execute using cutting-edge algorithms and tools.
• Evaluate and creatively apply ML algorithms beyond textbook solutions.

Skills

NLP

Machine Learning

Python

Information Retrieval

Statistical Modeling

Apache Spark

Education

PhD/BS/MS in CS/AI

Tools

Apache Spark

Python

TensorFlow/PyTorch

Job description

Media.net is a leading global ad tech company, building innovative products and solutions for advertisers and publishers. Media.net creates the most transparent and efficient path for ad budgets to become publisher revenue, while delivering meaningful value to advertisers, publishers, app developers, and end users across the open web.

We are one of the world’s largest independent contextual advertising businesses, with one of the industry’s most comprehensive advertising technology portfolios. Media.net powers major publishers and ad-tech platforms across formats, including display, video, mobile, native, search, and local. Our platform manages high-quality ad supply across 500,000+ websites and is licensed by the biggest publishers, ad networks, and technology partners worldwide.

Beyond our core advertising business, Media.net also operates at scale in performance-driven consumer acquisition, app development and distribution, and strategic technology investments. These efforts are supported by one of the world’s largest independent software development engines, enabling us to innovate rapidly and build products used across global markets.

Media.net is home to 1,300+ employees, with key operations across North America, Europe, and Asia. Our US headquarters is in New York, and our global headquarters is in Dubai. Across 50+ demand partners, 21K+ publishers, and a growing portfolio of products and services, we take great pride in driving trusted, transparent, and scalable results.

Data Science is at the heart of Media.net. The team uses advanced statistical and machine learning and deep learning models, large scale distributed computing along with tools from mathematics, economics, auction theory to build solutions that enable us to match users with relevant ads in the most optimal way thereby maximizing revenue for our customers and for Media.net.

Some of the challenges the team deals with:

• How do you use information retrieval, machine learning models to estimate click through rate and revenue given the information regarding the position of the slot, user device, location and content of the page. How do you scale the same for thousands of domains, millions of urls?
• How do you match ads to page views considering contextual information? How do you design learning mechanisms to continuously learn from user feedback in the form of clicks and conversions? How do you deal with the extremely sparse data? What do you do for new ads and new pages? How do we design better explore-exploit frameworks? How do you design learning algorithms that are fast and scalable?
• How do you combine contextual targeting with behavioral user-based targeting?
• How do you establish a unique user identity based on multiple noisy signals so that behavioral targeting is accurate?
• Can you use NLP to find more genetic trends based on the content of the page and ads?

What is in it for you?

• Understand business requirements, analyze and extract relevant information from large amounts of historical data.
• Use your knowledge of Information retrieval, NLP, Machine Learning (including Deep Learning) to build prototype solutions keeping scale, speed and accuracy in mind.
• Work with engineering teams to implement the prototype. Work with engineers to design appropriate model performance metrics and create reports to track the same.
• Work with the engineering teams to identify areas of improvement, jointly develop research agenda and execute on the same using cutting edge algorithms and tools.
• You will need to understand a broad range of ML algorithms and appreciation on how to apply them to complex practical problems. You will also need to have enough theoretical background and a good grasp of algorithms to be able to critically evaluate existing ML algorithms and be creative when there is a need to go beyond textbook solutions.

Who should apply for this role ?

• PhD/Research Degree or BS/MS in Computer Science, Statistics, Artificial Intelligence, Machine learning, Operations Research or related field.
• 6 years of experience in building Machine Learning/AI/Information Retrieval models
• Extensive knowledge and practical experience in machine learning, data mining, artificial intelligence, statistics.
• Understanding of supervised and unsupervised algorithms including but not limited to linear models, decision trees, random forests, gradient boosting machines etc.
• Excellent analytical and problem-solving abilities.
• Good knowledge of scientific programming in Python.
• Experience with Apache Spark is desired.

Bonus Points:

• Publications or presentation in recognized Machine Learning and Data Mining journals/conferences such as ICML
• Knowledge in several of the following: Math/math modeling, decision theory, fuzzy logic, Bayesian techniques, optimization techniques, statistical analysis of data, information retrieval, natural language processing, large scale data processing and data mining
• Ability deal with ambiguity & break them down into research problems