Lead Data Scientist

Title Lead Data Scientist

Position Overview

We are seeking a highly skilled Lead Data Scientist to drive our data strategy, AI/ML initiatives, and advanced analytics solutions across multiple client projects. The ideal candidate will be hands-on with model development, lead project delivery, and mentor a team of Data Scientists, ML Engineers, and Analysts. This role combines technical depth, product thinking, and stakeholder communication to deliver measurable business impact using data.

Key Responsibilities

• Own end-to-end data science lifecycle: problem framing, data ingestion, feature engineering, modeling, validation, deployment, and monitoring.
• Architect and implement scalable AI/ML solutions for OCR, document intelligence, RAG systems, predictive models, anomaly detection, recommendation systems, and generative AI use cases.
• Evaluate and adopt the latest LLMs (OpenAI, Llama, Claude, Gemini, etc.) and agentic workflows.
• Drive data science best practices, coding standards, experimentation frameworks, and MLOps processes.
• Build and optimize supervised, unsupervised, and deep learning models.
• Expertise in multimodal data handling — text, images, tabular data, timeseries, and PDFs.
• Implement hybrid search, embeddings, vector databases, and custom prompt-engineering for real-world retrieval systems.
• Develop pipelines using Python, PyTorch/TensorFlow, HuggingFace, LangChain/LlamaIndex, and cloud AI platforms.
• Work closely with Data Engineers to build robust ETL/ELT pipelines.
• Deploy models to production using CI/CD, versioning, monitoring, and model governance.
• Experience with AWS, Azure, or GCP services for ML (SageMaker, Vertex AI, Azure AI, Data Factory, etc.).
• Knowledge of scalable storage: S3, GCS, Azure Blob, BigQuery, Snowflake, Postgres, SQL/NoSQL.
• Work closely with business stakeholders to understand domain problems and translate them into machine learning opportunities.
• Lead technical discussions, pre-sales calls, demo sessions, and proposal creation.
• Create documentation, model performance reports, explainability insights, and business value communication.
• Mentor junior data scientists and analysts.
• Conduct code reviews, model evaluations, and continuous learning sessions.
• Define standards for data science reproducibility and experimentation.

Required Skills & Experience

Technical Skills

• 6–12 years of experience in data science / ML with at least 3+ years in a leadership role.
• Strong proficiency in Python, SQL, and data handling libraries (Pandas, NumPy, Scikit-learn).
• Deep understanding of ML algorithms: classification, regression, clustering, NLP, OCR, embeddings, LLMs, RAG, reinforcement learning, time series forecasting, etc.
• Experience building and deploying NLP solutions for document extraction, policy analysis, invoice parsing, and financial data intelligence is a plus.
• Familiarity with Vector Databases (Qdrant, Pinecone, FAISS, Chroma, Weaviate)
• Experience with multi-agent frameworks (LangGraph, CrewAI, OpenAI Agentforce preferred).
• Experience with Azure Document Intelligence, AWS Textract, or Google Document AI.
• Experience with Docker, Git, CI/CD pipelines, MLflow, Kubeflow, Airflow, etc.
• Ability to manage model lifecycle, versioning, tracking, and monitoring in production.
• Soft Skills
• Excellent communication and ability to explain complex models to non-technical stakeholders.
• Problem solver with strong analytical thinking and structured approach.
• Ability to manage multiple AI/ML projects parallelly, with ownership and accountability.