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Tech Stack
Tools & technologiesAzureDockerKubernetesPySparkPythonSQL
About the role
Key responsibilities & impact- Partner with stakeholders to clarify business questions into ML problem statements (classification, ranking, uplift, forecasting, optimization, GenAI RAG/agentic workflows, etc.).
- Write and maintain an ML System Design Spec: problem hypothesis, decision loop, users, constraints, acceptable risk, SLAs/SLOs, and post-deployment guardrails.
- Conduct advanced exploratory data analysis on large datasets using Python, pyspark, SQL, and visualization libraries.
- Design, implement, and validate machine learning and statistical models to address complex healthcare and insurance challenges.
- Collaborate with DevOps engineers to productionize models using containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines.
- Build and maintain reusable ML accelerators that standardize feature engineering, model training, and evaluation across tasks.
- Facilitate technical workshops and presentations to ensure clarity and buy-in across diverse audiences.
- Advocate for responsible AI by incorporating fairness, explainability, and bias detection into model development.
Requirements
What you’ll need- Bachelor’s/ master’s degree in data science, Statistics, Applied Mathematics, Computer Science, or a related field and around 8 to 10 years of industry experience
- Highly Preferred: PhD in a relevant quantitative field.
- Advanced certifications in Microsoft Azure and modern data/ML platform highly preferred.
- Strong proficiency in Python/ Pyspark (data wrangling, EDA, modeling) and SQL for working with large, complex datasets; advanced Excel for analysis and validation.
- Experience in defining evaluation taxonomies and acceptance criteria across initiatives; balances statistical and operational risk.
- Experience in codifing analytical playbooks and institutionalizes measurement frameworks across products/teams.
- Proven experience in balancing arbitrates trade-offs (accuracy, fairness, latency, interpretability) for high impact decisions.
- Proven track record of putting model into production and monitoring.
- Experience with Azure Databricks, Data bricks, for scalable data processing, model training, and orchestration.
- Knowledge of data privacy/security best practices across workflows.
- Knowledge of applying Responsible AI principles into model building, comprehensive documentation and audit trails for compliance experience.
- Experience in running multiple projects and conducting/overseeing high stakes experiments and peer reviews for critical models.
Benefits
Comp & perks- Health insurance
- Flexible working hours
- Professional development opportunities
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningstatistical modelsdata analysisPythonPysparkSQLExcelmodel productionfeature engineeringevaluation taxonomies
Soft Skills
stakeholder collaborationtechnical workshopscommunicationproblem-solvingadvocacy for responsible AIbalancing trade-offsproject managementpeer reviewsclarity in presentationsinstitutionalizing frameworks
Certifications
Bachelor's degreeMaster's degreePhDMicrosoft Azure certificationadvanced data/ML platform certification