KANINI - Lead Data Scientist - Machine Learning

About the role: We are 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 and engineers. You will be the senior technical authority the client turns to when a model decision needs defending. What you will 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 and 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: - 10 - 12 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, 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, confounder-aware analysis. - Feature engineering and 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 Great 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.