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
- Make and defend architecture-level calls: which model family, how much complexity is actually justified by the data and the business case, when a classical model beats a deep learning one and vice versa
- Design and run rigorous experiments (A/B tests, causal inference, uplift modeling) and be able to explain confounders and why an offline metric lied to you
- Build and own feature pipelines and training infrastructure that hold up at production scale and under data drift
- 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
- Production-grade feature engineering and training infrastructure experience, including experience with distributed training or large-scale compute (Spark, Ray, or equivalent)
- Experience owning a model in production long-term: monitoring, drift detection, retraining triggers, rollback
- Experience leading a team technically, including mentoring and setting the standard for others' modeling and code work
- Strong client-facing communication, able to hold a technical argument with a client stakeholder and explain a model's limitations plainly
- Cloud/ML platform stack Databricks, SageMaker, Vertex AI, Azure ML, etc.
- 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
- Technical leadership — mentoring, code/model review standards, setting team technical bars.
- Client communication — defending model tradeoffs and limitations to nontechnical stakeholders
- Python and SQL — production-grade Why Join KANINI?
- People-first culture with diversity and inclusion at its core.
- Recognized as a Outstanding Place to Work.
- Join KANINI’s award-winning Data Engineering Team, recognized as the "Outstanding Data Engineering Team" at DES 2025.
- Exposure to cutting-edge technologies: AI, Data Analytics, Cloud, IoT, Telehealth.
- Opportunities for career growth, mentorship, and impactful projects in Healthcare and BFSI domains. Ready to Make an Impact?
- Contribute to impactful projects that shape the future of data and AI
- Collaborate with top-tier professionals in a dynamic, fast-paced environment
- Take ownership of your work and make a tangible difference in the company’s success
- Grow your career with mentorship, training, and opportunities for advancement

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