Lead Data Scientist
Confirmed on the employer's own hiring board on Oct 28, 2026. First seen by Alion on Oct 26, 2026. Société Générale scores B on the Alion truth index.
Responsibilities
Lead Data Scientist
Responsibilities:
8 to 12 years of relevant experience, 4 years in data science domain.
• Leverage strong ML and AI engineering experience in complex data environments to achieve business objectives in innovative and efficient ways.
• Utilize a solid background in mathematics and statistics to guide model development, evaluation, and experimentation.
• Design, architect, and develop robust machine learning and LLM-based solutions, including agentic systems and multi-agent workflows where applicable.
• Develop scalable AI services and model-serving frameworks using REST APIs and FastAPI for enterprise integration.
• Establish monitoring, observability, and evaluation practices for ML and LLM systems, including performance tracking, drift detection, and response quality analysis.
• Define and implement end-to-end data and AI pipelines, including data ingestion, transformation, embedding generation, and vector database integration.
• Collaborate effectively within Agile Scrum teams, contributing to iterative development, technical leadership, and continuous improvement.
• Document business processes, system architectures, agent workflows, and technical frameworks clearly and comprehensively.
• Engage in close collaboration with various domains within the organization to ensure alignment with business needs and AI strategy.
• Participate in collaborative conceptualization sessions to design AI-first solutions leveraging LLMs, MCP, and agent communication protocols.
• Drive the adoption of AI agents, automation frameworks, and intelligent systems across business workflows.
Mandatory Skills:
• Proficiency in Python, Machine Learning, REST API development, FastAPI, and SQL.
• Experience designing and deploying production-grade ML and LLM systems in enterprise environments.
• Strong experience with data processing, cleansing, validation, and ensuring data integrity for analysis and model development.
• Conduct data quality checks, exploratory analysis, and performance evaluation to guide model and system design.
• Demonstrated expertise in building end-to-end ML pipelines, including data structures, transformations, and deployment pipelines.
• Strong understanding of statistical modelling techniques such as Regression, Clustering, Decision Trees, and Logistic Regression.
• Familiarity with machine learning algorithms such as KNN, Random Forests, Ensemble Methods, and probabilistic models.
• Experience with Large Language Models such as GPT, BERT, or similar architectures, including prompt engineering and solution design.
• Hands-on experience with vector databases, embeddings, and retrieval pipelines.
• Familiarity with agent frameworks and orchestration patterns such as LangChain, Semantic Kernel, AutoGen, or similar.
• Knowledge of observability and monitoring tools for AI systems, including logging, tracing, and telemetry.
• Strong understanding of microservices architecture and API-first design principles.
Preferred Skills:
• Experience designing and implementing AI agents, multi-agent systems, and tool-based reasoning workflows.
• Familiarity with Model Context Protocol (MCP) or similar agent communication and interoperability standards.
• Understanding of natural language processing (NLP) techniques and their application in enterprise use cases.
• Experience with LLMOps and GenAIOps practices, including prompt versioning, evaluation, and experiment tracking.
• Familiarity with advanced topics such as deep learning, transformers, fine-tuning, and reinforcement learning methods.
• Ability to design technical frameworks for diverse use cases, particularly those involving language understanding and agent-based automation.
• Experience building RAG pipelines and integrating knowledge retrieval systems.
• Identify opportunities to automate complex workflows using LLMs, agents, and AI-driven orchestration.
• Propose hypotheses and design experiments to solve business problems using ML and LLM capabilities.
Additional Responsibilities:
• Stay updated with the latest advancements in machine learning, LLMs, GenAI, agentic systems, and emerging protocols.
• Mentor junior team members and provide technical leadership in ML, LLM, and agent-based system design.
• Contribute to the development of best practices, design standards, and reusable frameworks for ML and LLM solutions.
• Drive innovation initiatives focused on AI agents, automation, and intelligent systems.
• Ensure adherence to responsible AI practices, including fairness, interpretability, and governance.
Profile required
Lead Data Scientist
8 to 12 years of relevant experience, 4 years in data science domain.
Responsibilities:
• Leverage strong ML and AI engineering experience in complex data environments to achieve business objectives in innovative and efficient ways.
• Utilize a solid background in mathematics and statistics to guide model development, evaluation, and experimentation.
• Design, architect, and develop robust machine learning and LLM-based solutions, including agentic systems and multi-agent workflows where applicable.
• Develop scalable AI services and model-serving frameworks using REST APIs and FastAPI for enterprise integration.
• Establish monitoring, observability, and evaluation practices for ML and LLM systems, including performance tracking, drift detection, and response quality analysis.
• Define and implement end-to-end data and AI pipelines, including data ingestion, transformation, embedding generation, and vector database integration.
• Collaborate effectively within Agile Scrum teams, contributing to iterative development, technical leadership, and continuous improvement.
• Document business processes, system architectures, agent workflows, and technical frameworks clearly and comprehensively.
• Engage in close collaboration with various domains within the organization to ensure alignment with business needs and AI strategy.
• Participate in collaborative conceptualization sessions to design AI-first solutions leveraging LLMs, MCP, and agent communication protocols.
• Drive the adoption of AI agents, automation frameworks, and intelligent systems across business workflows.
Mandatory Skills:
• Proficiency in Python, Machine Learning, REST API development, FastAPI, and SQL.
• Experience designing and deploying production-grade ML and LLM systems in enterprise environments.
• Strong experience with data processing, cleansing, validation, and ensuring data integrity for analysis and model development.
• Conduct data quality checks, exploratory analysis, and performance evaluation to guide model and system design.
• Demonstrated expertise in building end-to-end ML pipelines, including data structures, transformations, and deployment pipelines.
• Strong understanding of statistical modelling techniques such as Regression, Clustering, Decision Trees, and Logistic Regression.
• Familiarity with machine learning algorithms such as KNN, Random Forests, Ensemble Methods, and probabilistic models.
• Experience with Large Language Models such as GPT, BERT, or similar architectures, including prompt engineering and solution design.
• Hands-on experience with vector databases, embeddings, and retrieval pipelines.
• Familiarity with agent frameworks and orchestration patterns such as LangChain, Semantic Kernel, AutoGen, or similar.
• Knowledge of observability and monitoring tools for AI systems, including logging, tracing, and telemetry.
• Strong understanding of microservices architecture and API-first design principles.
Preferred Skills:
• Experience designing and implementing AI agents, multi-agent systems, and tool-based reasoning workflows.
• Familiarity with Model Context Protocol (MCP) or similar agent communication and interoperability standards.
• Understanding of natural language processing (NLP) techniques and their application in enterprise use cases.
• Experience with LLMOps and GenAIOps practices, including prompt versioning, evaluation, and experiment tracking.
• Familiarity with advanced topics such as deep learning, transformers, fine-tuning, and reinforcement learning methods.
• Ability to design technical frameworks for diverse use cases, particularly those involving language understanding and agent-based automation.
• Experience building RAG pipelines and integrating knowledge retrieval systems.
• Identify opportunities to automate complex workflows using LLMs, agents, and AI-driven orchestration.
• Propose hypotheses and design experiments to solve business problems using ML and LLM capabilities.
Additional Responsibilities:
• Stay updated with the latest advancements in machine learning, LLMs, GenAI, agentic systems, and emerging protocols.
• Mentor junior team members and provide technical leadership in ML, LLM, and agent-based system design.
• Contribute to the development of best practices, design standards, and reusable frameworks for ML and LLM solutions.
• Drive innovation initiatives focused on AI agents, automation, and intelligent systems.
• Ensure adherence to responsible AI practices, including fairness, interpretability, and governance.