Lead Data Scientist

Tredence; Bengaluru; Hybrid; INR 3,000,000 - 6,000,000; Full time

Job summary

Tredence is seeking a Lead Data Scientist who is highly hands-on, technically strong, and capable of leading a small team while owning end-to-end delivery of data science solutions. This role balances deep individual contribution (70-80%) with team leadership and stakeholder management (20-30%), driving innovations across classical ML and Generative AI, and ensuring production-ready deployments.

You will mentor juniors, collaborate with product and business leaders, and establish responsible AI

Qualifications

• 8–10 years in Data Science/ML/AI roles with measurable business impact
• Strong Python and SQL proficiency, plus experience with ML libraries and pipelines
• Hands-on with LLMs, embeddings, RAG, and GenAI techniques
• Proven leadership, mentoring, and delivery accountability
• Familiarity with cloud platforms (AWS/GCP/Azure) and production deployments

Responsibilities

• Lead end-to-end data science solutions from problem framing to production rollout
• Develop and optimize models using classical ML methods and time-series forecasting
• Oversee Generative AI initiatives, including LLMs, embeddings, and prompt engineering
• Mentor junior data scientists and align AI/ML roadmap with business goals
• Collaborate with product and business leaders to translate problems into measurable outcomes and ensure responsible AI practices

Skills

Python

SQL

ML/GenAI

Leadership

Cloud deployment

Tools

PyTorch

TensorFlow

Hugging Face

Job description

Lead Data Scientist

Role Overview

We are looking for a Lead Data Scientist who is highly hands-on, technically strong, and capable of leading a small team while owning end-to-end delivery of data science solutions. This role requires a balance of deep individual contribution (7080%) and team leadership & stakeholder management (20-30%). You will drive innovation across both classical machine learning and Generative AI initiatives, ensuring solutions move from experimentation to scalable production systems.

Key Responsibilities

• End-to-End Solutions: Design, develop, and deploy data science solutions from problem framing to production.
• Classical ML: Build and optimize models using regression, classification, clustering, time series forecasting, causal inference, price elasticity modeling, and optimization techniques.
• Generative AI: Develop and oversee solutions using LLMs, embeddings, RAG pipelines, fine-tuning, and prompt engineering.
• Hands-on Data Work: Work with SQL and Python for data extraction, analysis, feature engineering, and modeling.
• Leadership: Lead, mentor, and review work of junior and mid-level data scientists; define AI/ML roadmap aligned with business strategy.
• MLOps & Productionization: Partner with engineering to deploy models at scale, establish standards for monitoring, retraining, and cost optimization.
• Stakeholder Collaboration: Work closely with product managers and business leaders to translate problems into measurable outcomes and communicate insights effectively.
• Responsible AI: Ensure fairness, explainability, compliance, and ethical use of AI models.

Required Skills & Qualifications

• Experience: 8-10 years in Data Science/ML/AI roles, with proven business impact.
• Programming: Advanced proficiency in Python (pandas, numpy, scikit-learn, statsmodels, Hugging Face, PyTorch/TensorFlow) and strong SQL expertise (complex queries, performance tuning).
• ML & AI Expertise: Solid understanding of statistics, causal modeling, forecasting, optimization, and hands-on experience with LLMs and GenAI techniques.
• Leadership: Experience leading teams and owning delivery commitments.
• Cloud & Deployment: Familiarity with cloud platforms (AWS, GCP, Azure) and deploying models into production environments.
• Communication: Strong problem-solving and ability to explain complex concepts to non-technical stakeholders.

Preferred Qualifications

• Domain Expertise: Prior experience in retail, pricing, supply chain, or growth analytics.
• Optimization: Exposure to PuLP, OR-Tools, Gurobi.
• GenAI Platforms: Experience with OpenAI, Anthropic, Meta AI, or Google Cloud GenAI APIs.
• Vector Databases: Familiarity with FAISS, Pinecone, Weaviate, Milvus.
• MLOps Tools: Experience with MLflow, Kubeflow, Airflow.
• Applications: Background in NLP, conversational AI, search/recommendation systems, or enterprise GenAI applications.
• Contributions: Publications or open-source contributions in ML/GenAI.