Experience: 6–9 Years Location: [Noida]
Department: Data Science & Analytics
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.
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Lead the design, development, and deployment of machine learning and statistical models to solve high-impact business problems.
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Own the full ML lifecycle: data exploration, feature engineering, model development, validation, deployment, monitoring, and retraining.
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Mentor and guide a team of 3–6 data scientists; conduct code/model reviews and set best practices.
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Collaborate with product managers, data engineers, and business stakeholders to translate ambiguous business problems into well-defined data science solutions.
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Build and productionize ML pipelines in partnership with ML engineering teams (MLOps).
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Drive experimentation frameworks (A/B testing, causal inference) and define success metrics.
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Develop and fine-tune Generative AI / LLM-based solutions (RAG pipelines, prompt engineering, model fine-tuning) where applicable.
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Communicate insights and model outcomes to senior leadership through clear storytelling and visualization.
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Stay current with the latest research and evaluate new tools, techniques, and frameworks for adoption.
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Ensure model governance: fairness, explainability, reproducibility, and compliance.
Programming & Tools
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Expert-level Python (NumPy, Pandas, Scikit-learn, statsmodels); working knowledge of R is a plus
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Strong SQL (complex queries, window functions, query optimization)
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Version control with Git/GitHub/GitLab; comfort with CI/CD workflows
Machine Learning & Statistics
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Supervised/unsupervised learning: regression, classification, clustering, ensemble methods (XGBoost, LightGBM, CatBoost, Random Forests)
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Strong foundation in statistics: hypothesis testing, Bayesian methods, time-series forecasting (ARIMA, Prophet), causal inference, A/B experimentation
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Model evaluation, hyperparameter tuning (Optuna, GridSearch), and handling imbalanced data
Deep Learning & NLP
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Hands-on with TensorFlow / PyTorch / Keras
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NLP: transformers, Hugging Face, embeddings, text classification, NER
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Computer vision experience (CNNs, object detection) is a plus
Generative AI / LLMs
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Experience with LLMs (GPT, Claude, Llama, Gemini), prompt engineering, and fine-tuning (LoRA/PEFT)
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Building RAG pipelines with vector databases (Pinecone, FAISS, Weaviate, ChromaDB)
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Frameworks: LangChain, LlamaIndex
Big Data & Data Engineering
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Distributed computing: Apache Spark / PySpark, Databricks
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Data warehouses: Snowflake, BigQuery, Redshift
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Workflow orchestration: Airflow, Prefect, dbt
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Streaming (Kafka) exposure is a plus
MLOps & Deployment
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Model deployment: Docker, Kubernetes, FastAPI/Flask, REST APIs
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ML platforms: MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML
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Model monitoring, drift detection, and retraining pipelines
Cloud Platforms
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Strong experience with at least one: AWS / GCP / Azure (compute, storage, ML services)
Visualization & BI
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Tableau / Power BI / Looker, Plotly, Matplotlib, Seaborn
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Dashboarding and executive-level storytelling
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Bachelor's/Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field (PhD a plus)
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6–9 years of industry experience in data science, with at least 2 years in a senior or lead capacity
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Proven track record of deploying ML models to production with measurable business impact
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Experience mentoring junior data scientists and leading cross-functional projects
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Publications, patents, Kaggle achievements, or open-source contributions
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Domain experience in [e-commerce / fintech / healthcare / SaaS — customize]
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Experience with recommendation systems, fraud detection, or demand forecasting
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Familiarity with data privacy regulations (GDPR, etc.) and responsible AI practices
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Strong stakeholder management and executive communication
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Ability to translate business problems into analytical solutions
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Structured problem-solving, ownership mindset, and bias for action
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Team leadership, mentoring, and conflict resolution
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Competitive salary + performance bonus + ESOPs (customize)
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Flexible/hybrid working model
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Learning & development budget, conference sponsorships
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Health insurance and wellness benefits
