Data Scientist

Who We Are

Workhelix is an enterprise SaaS company with a single goal: helping organizations get the most out of their AI investments.

Our people and software combine to give leaders answers to the three critical questions about the business value of AI:

Where are our top opportunities to create value with AI?

Are our current efforts actually delivering? What’s their ROI?

How can we accelerate and increase the benefits of our AI?

To find answers for each customer, Workhelix applies our unmatched expertise in economics, AI, and data science. We have pioneered a task-scoring method to identify top GenAI opportunities, ensuring clear, data-driven deployment strategies. Our ROI monitoring suite applies Nobel Prize-winning economics to provide accurate, ongoing insights into the impact of GenAI implementations. Renowned AI and Economics thinkers including Andrew Ng, and cofounders Andy McAfee and Erik Brynjolfsson stand proudly behind our mission.

We maintain a culture established on five key principles. For more information on our leadership principles, please visit the careers page of our website.

Role

Workhelix is looking for a Data Scientist to own the analytical engine behind Nucleus, our core product, reporting directly to the Head of Engineering.

That engine starts by taking in different customer data sources and reconciling them into a structured, unified model. From there it runs the analyses that identify where AI can create value and how a company's current practices line up against those opportunities. The output is specific, actionable guidance: what to do next, for which teams, and why.

You will own that work end to end. You will also extend it; customers arrive with questions our existing analyses don't answer, and part of the job is deciding when to adapt something we have and when to build something new.

This is a production engineering role as much as an analytical one. You are not building notebooks for someone else to productionize. You write the code that ships, runs in containers, calls external services, and executes in our cloud infrastructure. You work in parallel with our product engineers and forward-deployed engineers, in their codebases as well as yours.

It is also a force-multiplier role. A meaningful part of your impact will come from building internal tools and raising the analytical fluency of the whole company, so that strategists, engineers, and leadership can answer their own questions instead of queuing behind you.

You are the only data scientist, so you set the roadmap. You will gather needs from forward-deployed strategists and product engineers, spot the patterns showing up across customers, decide what matters most, and sequence the work. This is an individual contributor role today with a clear path to leading a team as it grows.

Key Responsibilities:

What You’ll Do

Own the analytical pipeline

Own the data mart and the ETL that unifies disparate customer systems into a single model our analyses can run against

Build and maintain the AI assessment: task decomposition, task-level AI acceleration scoring, and comparison against observed AI usage

Design new analyses, adapted or custom, to answer questions specific customers bring us

Ship it as product

Write production code in Python and SQL that runs as part of the product, not alongside it

Orchestrate analytical workloads in cloud infrastructure (containers, step functions, external APIs)

Contribute directly to our product and platform codebases

Turn analysis into decisions

Build the visualizations and narratives that make findings land with technical and executive audiences alike

Work with customer stakeholders and internal teams to make sure methods are understood, trusted, and acted on

Uplevel the company

Build internal tools that let others run their own analyses and decide what those tools should be

Raise the analytical bar across the organization

Set direction

Translate needs from forward-deployed strategists and product engineers into a prioritized data science roadmap

Identify problems recurring across customers and decide what becomes core product versus custom work

What We Value

Willingness to own your work all the way into production on cloud infrastructure, including containerized and orchestrated workloads. Direct experience with AWS or equivalent is great; a track record of learning unfamiliar technical territory and getting it right is equally good

Strong data visualization and narrative skills; you can make a complex method legible to people who will never read the code

Comfort reading current academic literature and translating it into something that generates value in practice

Judgment about when the simple approach captures most of the value, and the discipline to ship it rather than overbuild

Thoughtful, self-aware use of AI in your own work: you move fast with it, you know where your own gaps are, and you own every decision it touches rather than deferring to it

Communication that earns trust with engineers, executives, and customer stakeholders. Prior client-facing experience is welcome but not required

What you bring

5+ years in data science, applied research, or a closely related field, with a demonstrated record of work that changed what a business did; not just analyses that were interesting, but analyses that were acted on

Recognized as a go-to voice on method and rigor wherever you've worked: the person others bring hard analytical questions to, and who sets the standard for how that work gets done

Strong programmatic thinking: structure, abstraction, failure modes, and architectural tradeoffs applied comfortably in Python and SQL. We care less about syntax than about whether you reason well about how code should be built, which matters more, not less, when AI is writing some of it

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

What Success Looks Like

First 30 days:

Understand the pipeline and data model end to end, how it works, where it's fragile, and why it was built the way it was

Explain what Workhelix does and why it matters without jargon, to a customer or a colleague

Meet the strategists and engineers who depend on this work and heard what customers are actually asking for

Run analytical work independently, including for customers whose data doesn't fit the existing shape

Have opinions about what's missing and where the method or the pipeline is weakest

By 60 days:

Made real contributions to the methods themselves, not just run the ones that exist

Something you built is in production and running against customer data

Shaping the data science roadmap and able to defend your priorities against competing asks

Comfortable fielding methodology questions from internal teams and from customer stakeholders

Know which recurring customer questions should become core product and which should stay custom

By 90 days:

Own the data science roadmap and the sequencing behind it

Other people can depend on what you've built, in product and internally

At least one piece of this work is legible enough that someone outside data science can answer their own question with it

Set the standard for analytical rigor at Workhelix, people bring you hard questions, and the answers hold up

Base salary 184-222K + Commission + Equity + Health Benefits

What You’ll Get

Competitive compensation package and benefits.

Chance to learn and collaborate with some of the most famous researchers in the AI and digital economy space: Andrew Ng, Erik Brynjolfsson, Andy McAfee, and Daniel Rock.

The uniq