Lead Data Scientist

Location: Bengaluru, India

Department: Projects & Delivery

Experience: 7+ years

Skills: LLM, Machine Learning, Data Science, Classical ML, Ai

Lead Data Scientist I/II

Experience: 7+ years | Location: Bengaluru (Hybrid)

Job Description: Focus on Classical Machine Learning (70%) and Generative AI (30%) & Team Mentorship.

Key Responsibilities: 

Team Mentorship and Stakeholder Management.

• Mentor, support and manage a cross-functional team of 4-6+ junior data scientists and MLEs’.
• Bring in structure across the client engagement - both internally as well as externally, with effective and top down communication.
• Act as the primary contact for clients, translating complex data needs into tasks. Present data insights to stakeholders, highlighting business impacts. Collaborate with cross-functional teams to align AI initiatives with business goals.
• Helping in Pre-Sales and New Client Activation.

Classical Machine Learning 

• Owning the entire workstreams end to end, from use-case identification, to initial designs & POC by building custom machine learning solutions as needed till the business impact calculation of the use-case while ensuring modularity, scalability, and production-ready codebase.
• Design and implement custom models, loss functions and be able to handle nuanced conversations of trade offs between various modelling choices.
• Apply specialized modeling for marketing scenarios (Targeting, Budget optimisation, Churn) and data limitations (Sparse/incomplete labels, Single class learning).

Generative AI & Large Language Models :

• Practical experience in building LLM-ready Data Management layers for large-scale structured and unstructured data.
• Apply foundational understanding of LLM Agents and multi-agent systems (e.g., Agent-Critique, ReACT, Agent Collaboration), advanced prompting, LLM evaluation, confidence grading, and Human-in-the-Loop systems.

Must Have Technical Skills 

Data Pipelines, PySpark & Databricks 

• Proficiency in Python and its data science ecosystem (NumPy, Pandas, Dask, PySpark) for large-scale data processing.
• Expert, hands-on experience with Databricks for MLOps, pipeline orchestration, and real-time deployment.
• Ability to perform effective feature engineering by understanding complex business objectives.

Core Machine Learning & Deep Learning

• In-depth knowledge of 
• Classical ML : Tree Based Models, GLMs’, Clustering Models etc.
• Deep Learning : ANN, 1D/2D/3D Convolutional Neural Networks (ConvNets), LSTMs, Transformer models.
• Strong proficiency in PU learning, single-class learning, representation learning, alongside traditional ML approaches.
• Advanced understanding and application of model explainability techniques (e.g., SHAP, LIME).
• Hands-on experience with ML/DL libraries such as Scikit-learn, TensorFlow/Keras, and PyTorch.

Others

• Experience utilizing large-scale language models (GPT-4, Mistral, Llama, Claude) through prompt engineering and custom fine tuning.
• Code Versioning Systems : Github, Git.

• Communication Skills : Of all the things, this is perhaps the most important soft skill for us, you must be able to capture the attention of your audience - usually in client calls, Succinctly put across your ideas to your team members, Bring clarity of thought and next steps to the table and present it well.
• Presentation Skills : Be able to visually present your ideas on a white board Be able to build a compelling presentation for CxOs in a top down manner with an angle of business impact in mind.
• Problem Solving Skills : Be able to leverage various internal tools, client datasets to craft a problem in the shortest time possible. Be able to make trade-offs keeping the timelines in mind.

Relevant to Have

• Background in Pharma Domain.
• Knowledge of Recommender Systems & Next Best Action Systems.

Benefits and Perks

• Competitive ESOP grants.
• Support for publishing papers and attending academic/industry conferences.
• High visibility across all functions at Akaike.