Company Context

Amida Technology Solutions is a healthcare technology company that builds data platforms and software for government and regulated healthcare organizations. The company works in an environment where data quality, interoperability, security, and measurable customer outcomes matter more than shipping consumer-style features quickly.

Problem

Amida is hiring Data Scientists, but the role is interpreted inconsistently across teams. Some leaders expect a research-oriented model builder, while others need a product-minded analyst who can turn messy healthcare data into deployable decision tools. This ambiguity creates hiring inefficiency, mismatched expectations, and slower product execution.

You are the product manager asked to define what a Data Scientist at Amida should be from a product perspective. Your goal is not to write an HR job description, but to shape a clear role definition that aligns user needs, business value, and product delivery. Assume Amida has three main internal customer groups: platform engineering, client delivery teams, and healthcare program stakeholders. Recent interviews show that 45% of projects involving data science were delayed because ownership between analytics, modeling, and productization was unclear.

Deliverables

1. Define the primary users or stakeholders of a Data Scientist at Amida and the jobs they need done.
2. Propose a product vision for the Data Scientist function: what problems this role should solve, and what it should explicitly not own.
3. Prioritize the top capabilities, workflows, or responsibilities this role should focus on in an MVP version of the function.
4. Recommend how Amida should measure whether this role definition is successful over the first 6-12 months.
5. Identify the key trade-offs in designing this role across research depth, delivery speed, compliance, and cross-functional ownership.

Constraints

• Amida can hire only 2 Data Scientists in the next 2 quarters.
• Most customer work involves regulated healthcare or public-sector data.
• Existing engineering teams are strong in pipelines and infrastructure, but weaker in experimentation and applied modeling.
• The role must show measurable impact within 6 months, not just long-term research potential.