The role, as published
About the company
Our client is a fast-growing technology company.
The role
Raydar is recruiting for this role on behalf of our client. Take responsibility for a handful of client relationships, shaping analytical studies and delivering automated machine learning workflows into production. Serve as the go-to analytical expert when meeting client stakeholders, and relay recurring needs to the product team.
What you'll do
- Lead a small set of client engagements from initial data onboarding and pilot planning through live operation.
- Plan and justify analytical studies that show clear business value.
- Create automated, client-specific workflows that train models, generate scores and deliver results on a regular basis.
- Take part in working sessions with client analytics, subject-matter and IT groups to convert business questions into data solutions.
- Notice recurring themes across clients and bring the most valuable needs to product planning.
What we're looking for
- At least 4 years of experience in data science or a related discipline.
- Confident client-facing skills, including leading working sessions alone and standing behind your approach when challenged.
- A track record of managing client relationships and speaking directly with customers.
- Experience planning and carrying out studies that produced measurable business results.
- Solid production Python skills, including pandas, scikit-learn and workflow orchestration: you deliver working code, not just notebooks.
- Hands-on work with large-scale distributed data processing for machine learning, using Spark.
- Background in healthcare or life sciences data, such as claims, health records, lab data or population health analytics.
- Skill in describing model behavior clearly and assessing results candidly.
- Experience delivering a sophisticated data or machine learning solution from design to launch.
- A self-starting mindset and readiness to take on varied responsibilities in a lean, fast-moving setting.
Bonus points
- Prior experience at an early-stage or rapidly scaling startup.
Location and work model
- Remote (US)
- Full-time
Compensation, as the posting describes it
- Equity
- Performance bonus