We’re hiring a
Lead Data Scientist
to join an
End-to-End Optimization
team focused on streamlining complex operational workflows that require a mix of
automation and human review
. In this role, you’ll design and build
production-grade data science solutions
that improve speed, accuracy, and efficiency across decision pipelines—partnering closely with product, engineering, and operations stakeholders to turn ambiguous problems into scalable systems.
What you’ll do
- Own end-to-end data science solutioning : problem framing → data exploration → modeling → deployment → monitoring and iteration
- Build and enhance predictive analytics / ML models in Python to optimize workflow outcomes and throughput
- Create monitoring and performance frameworks (data quality, drift, model performance, KPI health) and translate findings into actionable recommendations
- Break down complex business problems into measurable hypotheses, experiments, and deployable solutions
- Provide technical guidance and mentorship to 2–3 junior data scientists and data engineers
- Collaborate with engineers on productionization (APIs, batch pipelines, orchestration, CI/CD)
What we’re looking for
- Advanced proficiency in Python and strong foundations in predictive modeling / machine learning / statistical modeling
- Proven experience delivering end-to-end, production data science solutions (not just notebooks)
- Cloud experience in GCP, AWS, and/or Azure (GCP preferred)
- Strong software engineering and coding practices (version control, testing, scalable design)
- Experience in operations/logistics/process optimization (supply chain, call centers, workflow efficiency, etc.) is highly valued
- Excellent communication skills; ability to set vision and execute hands-on
Nice to have
- Healthcare experience (optional)
- Familiarity with MLOps patterns (model registries, automated retraining, observability, feature stores)
Tags & focus areas
Used for matching and alerts on DevFound Contract Machine Learning Data Science Ai