Senior / Lead Data Scientist

Overview

Job Description: Senior / Lead Data Scientist

• Experience: 6–9 Years
• Location: [Noida]
• Department: Data Science & Analytics

About the Role

We are looking for a Senior / Lead Data Scientist with 6–9 years of hands-on experience to drive end-to-end data science initiatives — from problem framing and experimentation to deploying production-grade ML solutions. You will lead a team of data scientists, partner with engineering and product stakeholders, and own the technical roadmap for advanced analytics and machine learning across the organization.

Key Responsibilities

1. Lead the design, development, and deployment of machine learning and statistical models to solve high-impact business problems.
2. Own the full ML lifecycle: data exploration, feature engineering, model development, validation, deployment, monitoring, and retraining.
3. Mentor and guide a team of 3–6 data scientists; conduct code/model reviews and set best practices.
4. Collaborate with product managers, data engineers, and business stakeholders to translate ambiguous business problems into well-defined data science solutions.
5. Build and productionize ML pipelines in partnership with ML engineering teams (MLOps).
6. Drive experimentation frameworks (A/B testing, causal inference) and define success metrics.
7. Develop and fine-tune Generative AI / LLM-based solutions (RAG pipelines, prompt engineering, model fine-tuning) where applicable.
8. Communicate insights and model outcomes to senior leadership through clear storytelling and visualization.
9. Stay current with the latest research and evaluate new tools, techniques, and frameworks for adoption.
10. Ensure model governance: fairness, explainability, reproducibility, and compliance.

Required Technical Skills
Programming & Tools

• Expert-level Python (NumPy, Pandas, Scikit-learn, statsmodels); working knowledge of R is a plus
• Strong SQL (complex queries, window functions, query optimization)
• Version control with Git/GitHub/GitLab; comfort with CI/CD workflows Machine Learning & Statistics Supervised/unsupervised learning: regression, classification, clustering, ensemble methods (XGBoost, LightGBM, CatBoost, Random Forests) Strong foundation in statistics: hypothesis testing, Bayesian methods, time-series forecasting (ARIMA, Prophet), causal inference, A/B experimentation Model evaluation, hyperparameter tuning (Optuna, GridSearch), and handling imbalanced data Deep Learning & NLP Hands-on with TensorFlow / PyTorch / Keras NLP: transformers, Hugging Face, embeddings, text classification, NER Computer vision experience (CNNs, object detection) is a plus Generative AI / LLMs Experience with LLMs (GPT, Claude, Llama, Gemini), prompt engineering, and fine-tuning (LoRA/PEFT) Building RAG pipelines with vector databases (Pinecone, FAISS, Weaviate, ChromaDB) Frameworks: LangChain, LlamaIndex Big Data & Data Engineering Distributed computing: Apache Spark / PySpark, Databricks Data warehouses: Snowflake, BigQuery, Redshift Workflow orchestration: Airflow, Prefect, dbt Streaming (Kafka) exposure is a plus MLOps & Deployment
• Model deployment: Docker, Kubernetes, FastAPI/Flask, REST APIs
• ML platforms: MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML
• Model monitoring, drift detection, and retraining pipelines Cloud Platforms
• Strong experience with at least one: AWS / GCP / Azure (compute, storage, ML services)
• Visualization & BI
• Tableau / Power BI / Looker, Plotly, Matplotlib, Seaborn
• Dashboarding and executive-level storytelling

Required Qualifications
Bachelor's/Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field (PhD a plus)

6–9 years of industry experience in data science, with at least 2 years in a senior or lead capacity
Proven track record of deploying ML models to production with measurable business impact
Experience mentoring junior data scientists and leading cross-functional projects
Preferred / Nice-to-Have
Publications, patents, Kaggle achievements, or open-source contributions
Domain experience in [e-commerce / fintech / healthcare / SaaS — customize]
Experience with recommendation systems, fraud detection, or demand forecasting
Familiarity with data privacy regulations (GDPR, etc.) and responsible AI practices

Soft Skills
Strong stakeholder management and executive communication
Ability to translate business problems into analytical solutions
Structured problem-solving, ownership mindset, and bias for action
Team leadership, mentoring, and conflict resolution

What We Offer
Competitive salary + performance bonus + ESOPs (customize)
Flexible/hybrid working model
Learning & development budget, conference sponsorships
Health insurance and wellness benefits