About the role

We're looking for a Lead Data Scientist who sets technical direction for a workstream, goes deep on the modeling and architecture decisions personally, and leads a small team of data scientists/engineers. You'll be the senior technical authority the client turns to when a model decision needs defending.

What you'll do

Own the full model lifecycle end to end: problem framing, feature engineering, model architecture selection, training at scale, validation, deployment, and post-launch monitoring and retraining.

Diagnose model degradation in production and make the retrain-versus-redesign call, including rollback plans

Set the technical bar for the team: code review standards, experiment tracking, model validation rigor, and mentor 2-4 data scientists/engineers against it

Be the primary technical point of contact for the client, translating ambiguous problems into scoped work and defending model tradeoffs and failure modes to a non-technical audience

Must-haves

8+ years of experience in data science, with demonstrated ownership of models from problem definition through production impact and measured business outcome

Deep, defensible grounding across model families: classical ML (regression, tree ensembles like XGBoost/LightGBM) and deep learning (architecture choice, training at scale), with clear judgment on when each is the right call Domain depth in at least one deep learning area relevant to enterprise work: NLP, forecasting, recommendation systems,



or computer vision, snowflake including hands-on architecture and training decisions, not just fine-tuning a pretrained model

Experience leading a team technically, including mentoring and setting the standard for others' modeling and code work

Solid client-facing communication, able to hold a technical argument with a client stakeholder and explain a model's limitations plainly cloud/ML platform stack

GenAI/LLM applied experience: retrieval design, evaluation harnesses, honesty about failure modes, not just demo projects

MLOps tooling depth: MLflow or similar model registries, automated retraining pipelines, CI/CD for ML Prior consulting or professional services background, comfortable across multiple concurrent client engagements

Primary Skills

Classical ML- regression, classification, tree ensembles (XGBoost/LightGBM), model selection judgment

Snowflake - Should be genuine hands-on experience working on Snowflake Platform.

Deep learning- architecture design and training at scale in at least one domain area (NLP,

forecasting, recommendation systems, or computer vision)

Causal inference / experimentation: A/B testing, uplift modeling, confounderaware analysis

Feature engineering & training infrastructure - production-scale pipelines, distributed compute (Spark, Ray, or equivalent)

MLOps / production ownership - deployment, drift and degradation monitoring, retraining triggers, rollback

📌 Data Scientist (Bengaluru)
🏢 kanini software solutions
📍 Bengaluru