{"id":3282436,"url":"https://alion.io/job/societe-generale-lead-data-scientist-3","title":"Lead Data Scientist","company":{"id":1760180,"name":"Société Générale","domain":"societegenerale.com","url":"https://alion.io/company/societegenerale-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career sitemap","truth_index":{"grade":"B","score":81,"open_postings":103,"ghost_share":0,"stale_share":0.961,"repost_share":0,"time_to_fill_p50_days":17,"computed_at":"2026-11-01T06:10:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","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":30000,"max_usd":65000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":20},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"BERT","optional":false},{"name":"Embeddings","optional":false},{"name":"FastAPI","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Rest API","optional":false},{"name":"Scrum","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"SQL","optional":false},{"name":"Fine-tuning","optional":true},{"name":"Interpretability","optional":true},{"name":"LLMOps","optional":true},{"name":"NLP","optional":true},{"name":"RAG","optional":true},{"name":"Reinforcement Learning","optional":true}],"status":"live","first_seen_at":"2026-10-26T17:05:58Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-11-01T06:31:46Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"Responsibilities\n\n Lead Data Scientist\n Responsibilities:\n8 to 12 years of relevant experience, 4 years in data science domain.\n• Leverage strong ML and AI engineering experience in complex data environments to achieve business objectives in innovative and efficient ways.\n• Utilize a solid background in mathematics and statistics to guide model development, evaluation, and experimentation.\n• Design, architect, and develop robust machine learning and LLM-based solutions, including agentic systems and multi-agent workflows where applicable.\n• Develop scalable AI services and model-serving frameworks using REST APIs and FastAPI for enterprise integration.\n• Establish monitoring, observability, and evaluation practices for ML and LLM systems, including performance tracking, drift detection, and response quality analysis.\n• Define and implement end-to-end data and AI pipelines, including data ingestion, transformation, embedding generation, and vector database integration.\n• Collaborate effectively within Agile Scrum teams, contributing to iterative development, technical leadership, and continuous improvement.\n• Document business processes, system architectures, agent workflows, and technical frameworks clearly and comprehensively.\n• Engage in close collaboration with various domains within the organization to ensure alignment with business needs and AI strategy.\n• Participate in collaborative conceptualization sessions to design AI-first solutions leveraging LLMs, MCP, and agent communication protocols.\n• Drive the adoption of AI agents, automation frameworks, and intelligent systems across business workflows.\nMandatory Skills:\n• Proficiency in Python, Machine Learning, REST API development, FastAPI, and SQL.\n• Experience designing and deploying production-grade ML and LLM systems in enterprise environments.\n• Strong experience with data processing, cleansing, validation, and ensuring data integrity for analysis and model development.\n• Conduct data quality checks, exploratory analysis, and performance evaluation to guide model and system design.\n• Demonstrated expertise in building end-to-end ML pipelines, including data structures, transformations, and deployment pipelines.\n• Strong understanding of statistical modelling techniques such as Regression, Clustering, Decision Trees, and Logistic Regression.\n• Familiarity with machine learning algorithms such as KNN, Random Forests, Ensemble Methods, and probabilistic models.\n• Experience with Large Language Models such as GPT, BERT, or similar architectures, including prompt engineering and solution design.\n• Hands-on experience with vector databases, embeddings, and retrieval pipelines.\n• Familiarity with agent frameworks and orchestration patterns such as LangChain, Semantic Kernel, AutoGen, or similar.\n• Knowledge of observability and monitoring tools for AI systems, including logging, tracing, and telemetry.\n• Strong understanding of microservices architecture and API-first design principles.\nPreferred Skills:\n• Experience designing and implementing AI agents, multi-agent systems, and tool-based reasoning workflows.\n• Familiarity with Model Context Protocol (MCP) or similar agent communication and interoperability standards.\n• Understanding of natural language processing (NLP) techniques and their application in enterprise use cases.\n• Experience with LLMOps and GenAIOps practices, including prompt versioning, evaluation, and experiment tracking.\n• Familiarity with advanced topics such as deep learning, transformers, fine-tuning, and reinforcement learning methods.\n• Ability to design technical frameworks for diverse use cases, particularly those involving language understanding and agent-based automation.\n• Experience building RAG pipelines and integrating knowledge retrieval systems.\n• Identify opportunities to automate complex workflows using LLMs, agents, and AI-driven orchestration.\n• Propose hypotheses and design experiments to solve business problems using ML and LLM capabilities.\nAdditional Responsibilities:\n• Stay updated with the latest advancements in machine learning, LLMs, GenAI, agentic systems, and emerging protocols.\n• Mentor junior team members and provide technical leadership in ML, LLM, and agent-based system design.\n• Contribute to the development of best practices, design standards, and reusable frameworks for ML and LLM solutions.\n• Drive innovation initiatives focused on AI agents, automation, and intelligent systems.\n• Ensure adherence to responsible AI practices, including fairness, interpretability, and governance.\n\n Profile required\n\n Lead Data Scientist\n8 to 12 years of relevant experience, 4 years in data science domain.\nResponsibilities:\n\n• Leverage strong ML and AI engineering experience in complex data environments to achieve business objectives in innovative and efficient ways.\n\n• Utilize a solid background in mathematics and statistics to guide model development, evaluation, and experimentation.\n\n• Design, architect, and develop robust machine learning and LLM-based solutions, including agentic systems and multi-agent workflows where applicable.\n\n• Develop scalable AI services and model-serving frameworks using REST APIs and FastAPI for enterprise integration.\n\n• Establish monitoring, observability, and evaluation practices for ML and LLM systems, including performance tracking, drift detection, and response quality analysis.\n\n• Define and implement end-to-end data and AI pipelines, including data ingestion, transformation, embedding generation, and vector database integration.\n\n• Collaborate effectively within Agile Scrum teams, contributing to iterative development, technical leadership, and continuous improvement.\n\n• Document business processes, system architectures, agent workflows, and technical frameworks clearly and comprehensively.\n\n• Engage in close collaboration with various domains within the organization to ensure alignment with business needs and AI strategy.\n\n• Participate in collaborative conceptualization sessions to design AI-first solutions leveraging LLMs, MCP, and agent communication protocols.\n\n• Drive the adoption of AI agents, automation frameworks, and intelligent systems across business workflows.\n\nMandatory Skills:\n\n• Proficiency in Python, Machine Learning, REST API development, FastAPI, and SQL.\n\n• Experience designing and deploying production-grade ML and LLM systems in enterprise environments.\n\n• Strong experience with data processing, cleansing, validation, and ensuring data integrity for analysis and model development.\n\n• Conduct data quality checks, exploratory analysis, and performance evaluation to guide model and system design.\n\n• Demonstrated expertise in building end-to-end ML pipelines, including data structures, transformations, and deployment pipelines.\n\n• Strong understanding of statistical modelling techniques such as Regression, Clustering, Decision Trees, and Logistic Regression.\n\n• Familiarity with machine learning algorithms such as KNN, Random Forests, Ensemble Methods, and probabilistic models.\n\n• Experience with Large Language Models such as GPT, BERT, or similar architectures, including prompt engineering and solution design.\n\n• Hands-on experience with vector databases, embeddings, and retrieval pipelines.\n\n• Familiarity with agent frameworks and orchestration patterns such as LangChain, Semantic Kernel, AutoGen, or similar.\n\n• Knowledge of observability and monitoring tools for AI systems, including logging, tracing, and telemetry.\n\n• Strong understanding of microservices architecture and API-first design principles.\n\nPreferred Skills:\n\n• Experience designing and implementing AI agents, multi-agent systems, and tool-based reasoning workflows.\n\n• Familiarity with Model Context Protocol (MCP) or similar agent communication and interoperability standards.\n\n• Understanding of natural language processing (NLP) techniques and their application in enterprise use cases.\n\n• Experience with LLMOps and GenAIOps practices, including prompt versioning, evaluation, and experiment tracking.\n\n• Familiarity with advanced topics such as deep learning, transformers, fine-tuning, and reinforcement learning methods.\n\n• Ability to design technical frameworks for diverse use cases, particularly those involving language understanding and agent-based automation.\n\n• Experience building RAG pipelines and integrating knowledge retrieval systems.\n\n• Identify opportunities to automate complex workflows using LLMs, agents, and AI-driven orchestration.\n\n• Propose hypotheses and design experiments to solve business problems using ML and LLM capabilities.\n\nAdditional Responsibilities:\n\n• Stay updated with the latest advancements in machine learning, LLMs, GenAI, agentic systems, and emerging protocols.\n\n• Mentor junior team members and provide technical leadership in ML, LLM, and agent-based system design.\n\n• Contribute to the development of best practices, design standards, and reusable frameworks for ML and LLM solutions.\n\n• Drive innovation initiatives focused on AI agents, automation, and intelligent systems.\n\n• Ensure adherence to responsible AI practices, including fairness, interpretability, and 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