{"id":1226200,"url":"https://alion.io/job/kanini-software-solutions-lead-data-scientist","title":"Lead Data Scientist","company":{"id":3800175,"name":"KANINI Software Solutions","domain":"kanini.com","url":"https://alion.io/company/kanini-software-solutions","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, India","Bengaluru, India","Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":48000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":16},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"Databricks","optional":false},{"name":"Fine-tuning","optional":false},{"name":"LightGBM","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Snowflake","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Vertex AI","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-09-22T07:49:36Z","employer_posted_date":null,"last_verified_at":"2026-09-22T07:49:36Z","board_verified":false,"closed_at":null,"days_open":6,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":6},"description":"About the role:\n\nWe 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.\n\nWhat you will do:\n\n- 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.\n\n- 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.\n\n- 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.\n\n- Build and own feature pipelines and training infrastructure that hold up at production scale and under data drift.\n\n- Diagnose model degradation in production and make the retrain-versus-redesign call, including rollback plans.\n\n- 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.\n\n- 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.\n\nMust-haves:\n\n- 10 - 12 years of experience in data science, with demonstrated ownership of models from problem definition through production impact and measured business outcome.\n\n- 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.\n\n- 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.\n\n- Production-grade feature engineering and training infrastructure experience, including experience with distributed training or large-scale compute (Spark, Ray, or equivalent).\n\n- Experience owning a model in production long-term: monitoring, drift detection, retraining triggers, rollback.\n\n- Experience leading a team technically, including mentoring and setting the standard for others' modeling and code work.\n\n- Strong client-facing communication, able to hold a technical argument with a client stakeholder and explain a model's limitations plainly.\n\n- Cloud/ML platform stack - Databricks, SageMaker, Vertex AI, Azure ML, etc.\n\n- GenAI/LLM applied experience: retrieval design, evaluation harnesses, honesty about failure modes, not just demo projects.\n\n- MLOps tooling depth: MLflow or similar model registries, automated retraining pipelines, CI/CD for ML.\n\n- Prior consulting or professional services background, comfortable across multiple concurrent client engagements.\n\nPrimary Skills:\n\n- Classical ML - regression, classification, tree ensembles (XGBoost/LightGBM), model selection judgment.\n\n- Snowflake - Should be genuine hands-on experience working on Snowflake Platform.\n\n- Deep learning - architecture design and training at scale in at least one domain area (NLP, forecasting, recommendation systems, or computer vision).\n\n- Causal inference / experimentation - A/B testing, uplift modeling, confounder-aware analysis.\n\n- Feature engineering and training infrastructure - production-scale pipelines, distributed compute (Spark, Ray, or equivalent).\n\n- MLOps / production ownership - deployment, drift and degradation monitoring, retraining triggers, rollback.\n\n- Technical leadership - mentoring, code/model review standards, setting team technical bars.\n\n- Client communication - defending model tradeoffs and limitations to nontechnical stakeholders.\n\n- Python and SQL - production-grade.\n\nWhy Join KANINI?:\n\n- People-first culture with diversity and inclusion at its core.\n\n- Recognized as a Great Place to Work.\n\n- Join KANINI's award-winning Data Engineering Team, recognized as the \"Outstanding Data Engineering Team\" at DES 2025.\n\n- Exposure to cutting-edge technologies: AI, Data Analytics, Cloud, IoT, Telehealth.\n\n- Opportunities for career growth, mentorship, and impactful projects in Healthcare and BFSI domains.\n\nReady to Make an Impact?:\n\n- Contribute to impactful projects that shape the future of data and AI.\n\n- Collaborate with top-tier professionals in a dynamic, fast-paced environment.\n\n- Take ownership of your work and make a tangible difference in the company's success.\n\n- Grow your career with mentorship, training, and opportunities for advancement.\n\nSkills\nData Science, Analytics, Machine Learning, Data Analytics, Artificial Intelligence, Data Scientist, SQL, Python","description_format":"text","description_chars":4962,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","Enterprise Apps Implementation Partners","Data Engineering & Migration Services"],"lifecycle":[{"event":"open","at":"2026-09-25T13:06:44Z"}],"liveness":{"score":85,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.849,"p_room":1,"age_days":5,"expected_fill_days":23,"reasons":["seen:5","velocity","win:early"],"computed_at":"2026-09-28T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/kanini-software-solutions-lead-data-scientist","json_url":"https://alion.io/job/kanini-software-solutions-lead-data-scientist.json","meta":{"generated_at":"2026-09-28T23:31:48Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1189,"day_limit":5000,"remaining_today":3811,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}