Data Scientist

Workhelix•San Francisco, CA

6d•$184,000 - $222,000

About The Position

Workhelix is an enterprise SaaS company focused on helping organizations maximize their AI investments. Our approach combines expertise in economics, AI, and data science to answer critical questions about the business value of AI, including identifying top opportunities, measuring ROI of current efforts, and accelerating AI benefits. We utilize a unique task-scoring method for GenAI opportunities and apply Nobel Prize-winning economics for ROI monitoring. Our culture is built on five key principles.

Requirements

5+ years in data science, applied research, or a closely related field, with a demonstrated record of work that changed business actions.
Recognized as a go-to voice on method and rigor, setting the standard for analytical work.
Strong programmatic thinking in Python and SQL, including structure, abstraction, failure modes, and architectural tradeoffs.
Strong data modeling instincts and experience with modern transformation or semantic layer tooling (dbt, Looker, or similar).
Depth across machine learning and predictive analytics, NLP including text embeddings and inference, and clustering methods such as hierarchical clustering.
Working knowledge of causal inference from observational data and quasi-experimental design.
Willingness to own work all the way into production on cloud infrastructure, including containerized and orchestrated workloads.
Comfort reading current academic literature and translating it into practical value.
Judgment about when a simple approach captures most of the value and the discipline to ship it rather than overbuild.
Thoughtful, self-aware use of AI in own work, moving fast with it, knowing personal gaps, and owning decisions it touches.
Communication that earns trust with engineers, executives, and customer stakeholders.

Nice To Haves

Direct experience with AWS or equivalent.
Prior client-facing experience.

Responsibilities

Own the analytical engine behind Nucleus, the core product, which involves taking in customer data sources, reconciling them into a structured model, and running analyses to identify AI value creation opportunities and current practice alignment.
Extend existing analyses and build new ones to answer customer-specific questions.
Write production code in Python and SQL that runs in containers, calls external services, and executes in cloud infrastructure, contributing to product and platform codebases.
Orchestrate analytical workloads in cloud infrastructure (containers, step functions, external APIs).
Build visualizations and narratives to communicate findings to technical and executive audiences.
Work with customer stakeholders and internal teams to ensure methods are understood, trusted, and acted upon.
Build internal tools to empower others to run their own analyses and raise the analytical fluency of the company.
Translate needs from forward-deployed strategists and product engineers into a prioritized data science roadmap.
Identify recurring problems across customers and decide what becomes core product versus custom work.