{"id":3873449,"url":"https://alion.io/job/weekday-sr-data-scientist","title":"Sr Data Scientist","company":{"id":7097,"name":"Weekday","domain":"weekday.works","url":"https://alion.io/company/weekday","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":81,"open_postings":93,"ghost_share":0,"stale_share":0.978,"repost_share":0,"time_to_fill_p50_days":6,"computed_at":"2026-11-06T05:58:05Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Hyderabad, India","Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CNN","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Sentiment Analysis","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Tokenization","optional":false},{"name":"Transformers","optional":false}],"status":"live","first_seen_at":"2026-11-06T15:23:31Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-07T05:09:13Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"'\n: - ( - )\nExperience: 2+ yrs\nLocation: Hyderabad, Telangana, India, Bengaluru\nAs a Sr Data Scientist, you will spearhead advanced analytical initiatives that fuse Operations Research, Natural Language Processing, and Deep Learning to unlock actionable insights and drive decision-making across multiple domains. Your role will involve designing and implementing end-to-end machine learning pipelines, leveraging Python and SQL to extract, clean, and model complex datasets. You will collaborate closely with cross-functional teams, translating business challenges into data-driven solutions and ensuring the scalability and robustness of solutions in production environments. With a focus on probabilistic modeling and optimization, you will continually refine models, evaluate performance, and communicate findings to both technical and non-technical stakeholders.\nRequirements\nDevelop and deploy scalable operations research models for resource allocation and logistics optimization.\nEngineer end-to-end NLP pipelines for text mining, sentiment analysis, and unstructured data transformation.\nDesign, train, and fine-tune deep learning architectures (CNN, RNN, Transformer) for vision and language tasks.\nWrite efficient, maintainable Python code and complex SQL queries to support data extraction and analysis.\nConduct rigorous statistical analysis and probabilistic modeling to quantify uncertainty and risk.\nCollaborate with data engineers to ensure data quality, pipeline reliability, and system performance.\nPerform benchmark studies and comparative evaluations of algorithms and models.\nMentor junior data scientists and provide technical guidance on best practices.\nStay current with emerging research, tools, and techniques in operations research and NLP.\nCommunicate insights and recommendations through executive-level presentations and reports.\n2+ years of industry experience as a senior data scientist or equivalent role.\nDeep expertise in operations research, including linear programming, stochastic optimization, and simulation.\nProven track record in natural language processing, with experience in tokenization, embeddings, and transformer-based models.\nHands-on mastery of deep learning frameworks such as TensorFlow or PyTorch.\nStrong programming proficiency in Python, along with advanced SQL querying skills.\nSolid foundation in probability theory and Bayesian statistics.\nExperience deploying models in production using Docker, Kubernetes, or similar container orchestration tools.\nExcellent problem-solving skills and the ability to translate complex data problems into clear, actionable solutions.\nStrong communication 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