Senior / Lead Data Scientist
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Senior / Lead Data Scientist

INTERRA INFORMATION TECHNOLOGIES INDIA PRIVATE LIMITED
Noida, Uttar Pradesh

Quick apply

Job details

Benefits

Health insurance

Qualifications

TensorFlow
CI/CD
Power BI
Image processing
Azure
Statistics
Law
Doctoral degree
Computer Science
Kubernetes
Computer vision
Big data
Spark
NumPy
R
Git
Tableau
Master's degree
SQL
Pandas
AWS
Docker
Bachelor's degree
Machine learning
Distributed systems
Continuous integration
REST
Mentoring
Natural language processing
GitHub
APIs
Fraud prevention and detection
Kafka
Data science
Keras
Redshift
Flask
SaaS
AI
Leadership
GitLab
Communication skills
Python

Full job description

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

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

Quick apply

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