{"id":3241979,"url":"https://alion.io/job/media-net-data-scientist","title":"Data Scientist","company":{"id":4586,"name":"Media.net","domain":"media.net","url":"https://alion.io/company/media-net","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":13500,"max_usd":31000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":12},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false}],"status":"live","first_seen_at":"2026-10-25T16:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-25T16:35:00Z","board_verified":false,"closed_at":null,"days_open":5,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":5},"description":"Data Science is at the heart of Media.net . The team uses advanced statistical and machine learning and deep learning models and large-scale distributed computing along with tools from mathematics, economics, and 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 .\n\nResponsibilities: \nUnderstand business requirements and analyze and extract relevant information from large amounts of historical data.\nUse your knowledge of Information retrieval, NLP, and Machine Learning (including Deep Learning) to build prototype solutions, keeping scale, speed, and accuracy in mind.\nWork with engineering teams to implement the prototype. Work with engineers to design appropriate model performance metrics and create reports to track the same.\nWork with the engineering teams to identify areas of improvement, jointly develop a research agenda and execute on the same using cutting-edge algorithms and tools.\nYou will need to understand a broad range of ML algorithms and appreciate how to apply them to complex practical problems.\nYou 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.\n\nRequirements: \nPhD/Research Degree or BS/MS in Computer Science, Statistics, Artificial Intelligence, Machine learning, Operations Research, or a related field.\n2 - 5 years of experience in building Machine Learning/AI/Information Retrieval models\nExtensive knowledge and practical experience in machine learning, data mining, artificial intelligence, and statistics.\nUnderstanding of supervised and unsupervised algorithms, including but not limited to linear models, decision trees, random forests, gradient boosting machines, etc.\nExcellent analytical and problem-solving abilities.\nGood knowledge of scientific programming in Python.\nExperience with Apache Spark is desired.\nExcellent verbal and written communication skills\n\nBonus Points: \nPublications or presentations in recognized Machine Learning and Data Mining journals/conferences such as ICML.\nKnowledge 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.\nAbility to deal with ambiguity and break it down into research problems.\nStrong theoretical and research acumen.\nSkills\nAI, ML, NLP, artificial intelligence, deep learning, information retrieval, machine learning, natural language processing, 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