Senior Data Scientist, Workera
AI Scoring & Evaluation
Skills intelligence platform
Be an early applicant
Skills intelligence platform
201-500 employees
Be an early applicant
201-500 employees
Company mission
To empower individuals and organizations to thrive by understanding, measuring, and improving their skills - across any skill - at any time.
Role
Who you are
- If you want a role where your judgment becomes the thing customers, auditors, and your own engineering team rely on, and where the measurement problems are still being invented rather than optimized, this is it
- We're looking for signal, not checkboxes. In rough order of what matters most:
- You've driven production data science work independently
- 4+ years (or 3+ with demonstrated end-to-end ownership) of a production data project ideally in an AI startup or fast-moving product environment where you scoped investigations yourself rather than picking up defined tickets
- Python and SQL are assumed; you shouldn't need an engineer to run an analysis
- Real statistical and analytical depth. You can turn, "is our scoring reliable?" into a defined study with a defensible method and a clear answer fast, and without being handed the design
- You can read this kind of data. Quantitative work with educational, learning, or assessment data or a clear appetite to go deep on it fast. We're looking for someone who can interpret our assessment data fluently and wants to become expert in how it's built
- You've worked with LLM-based systems in production. Prompt design, evaluating model output against human judgment, and a real sense of where these systems fail
- You explain quantitative results to non-technical audiences, including executives and customers, without losing the nuance
- A plus, not a requirement: background in psychometrics or educational measurement. Our assessment scientists will coach you — but you should want to learn it
What the job involves
- As Workera moves into higher-stakes decisions, you're the person who can prove a measurement is accurate, fair, and defensible
- You'll own the measurement system that turns signals into the skill data our customers act on: the evaluation harnesses, the calibration methods, the quality monitoring behind every score
- You'll also shape how our AI software itself is designed, from how components are orchestrated and prompted to the evaluation frameworks that decide whether they're good enough to ship
- You're not just running analyses, you're building the trust layer underneath a product that increasingly makes high-stakes calls about people's careers
- This role sits at the intersection of Engineering, Assessment Science, and Design, partnering closely with the product engineers who build our scoring pipeline
- You're the data layer that pieces those disciplines together: the foundation the rest of our scoring is built on, and the reason a construct definition from Assessment Science actually turns into a number a customer can trust
- You report to Dr. Taylor Sullivan, Workera’s VP of Product and Assessments, and work day to day with two groups: The assessment science team decides what we're measuring and how a skill turns into test content, they own construct definition (what "good at X" actually means), blueprinting (how an assessment is structured), and content authoring (writing the actual questions)
- You partner with them on how scoring methods serve that intent. The assessment tech team builds and maintains the scoring pipeline; you work embedded with them: writing specs, implementing and coordinating improvements, and validating that what ships meets the quality bar
- You also work with data engineering on data availability. It's a small, cross-functional team, and you sit inside product decisions rather than in a separate research function
- Build trust by owning our scoring mechanism
- Own scoring quality end to end: evaluator design, rubric anchoring, calibration, and accountability for accuracy against expert benchmarks
- Build and run the continuous evaluation harness: gold sets, bias diagnostics, drift detection, and the pre-release gate that every scoring change must clear
- Define and publish assessment quality KPIs (human to AI agreement, reliability, classification accuracy, bias indicators, latency, cost per assessment) on dashboards anyone in the company can reference
- Lead the analytics investigations behind assessment decisions: performance studies, impact simulations, root cause analysis when scores look wrong
- Ship measurement improvements end to end: implement and coordinate with tech, own the spec, the validation, and the quality bar in both cases
- Be Workera's liaison between AI governance and InfoSec: stay current on frameworks like GDPR and the EU AI Act, and turn that into the evidence we show in audits, security reviews, and enterprise diligence when customers ask how we use AI in our assessments
- Empower partners across Product, Engineering, and GTM to get the data and analysis they need autonomously, by building AI tooling and documentation rather than answering each request yourself
- We don't expect you to figure it out alone. Here's what great looks like at each stage:
- First 30 Days: Learn the Machine
- Immerse yourself in Workera's platform, customers, and the problems we're solving. You'll shadow key workflows and understand how AI is embedded in day-to-day operations across teams
- Deliver at least one analytics investigation that changes a product decision
- Sit in on an enterprise or auditor conversation about score quality to see what evidence customers actually ask for
- By 60 Days: Ship Something Real
- Own your first meaningful deliverable and demonstrate end-to-end execution
- Troubleshoot scoring end to end independently
- Ship one measurable scoring improvement, validated against expert labelled data, through the pre-release gate
- By 90 Days: Multiply Your Impact
- Operate with full autonomy in your domain; your team relies on your judgment
- Have built or deployed at least one AI-assisted workflow that the team adopts
- Set the scoring roadmap: identify the gaps, prioritise what gets built, and drive those initiatives to completion with Engineering
- Build systems that improve themselves: evaluation loops that flag their own drift, calibration that updates on new expert labels, tooling that gets better as it is used
- We're a fast-moving company -- the scope and shape of this role will evolve as we do
- At Workera, AI isn't a feature we sell -- it's how we operate. Every team member is expected to:
- Use AI daily. AI assistants, copilots, and automation tools are part of your stack -- not optional extras. We expect you to actively experiment with new tools and push the boundary of what's possible in your function
- Build your own leverage. Our marketers write code. Our PMs build automations. Our ops team deploys agents. If a workflow can be automated, you're expected to automate it
- Think in systems, not tasks. We value people who build repeatable, scalable solutions over people who grind through one-off work. Your goal is to make your function run smarter, not just harder
Our take
Companies are struggling to find a learning platform that can upskill and reskill their talent quickly, in accordance with the fast development of the technology used in different key roles such as data science, machine learning, and artificial intelligence.
Workera is an upskilling startup that helps companies evaluate their data and AI capabilities and provide their employees with personalized learning plans to keep them updated on the latest technologies and practices. The company uses AI and machine learning to evaluate the current skillset's within a company and to design a learning plan that will help businesses' workforce reach a particular level of understanding of new technology.
Recently, the company strengthened its enterprise footprint through a partnership with Udemy. The collaboration brings together Workera's AI-powered skills assessment with Udemy's global learning marketplace, creating an end-to-end solution that allows organizations to identify capability gaps and deliver more targeted training.
As demand for GenAI and advanced technical skills continues to grow, this partnership puts Workera at the centre of a more integrated, data-driven approach to workforce development, helping enterprise move beyond just one-off courses and toward continuous upskilling.
Steph
Company Specialist at Welcome to the Jungle
Insights
52% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
Mar 2023
$23.5m
SERIES B
Aug 2021
$16m
SERIES A
Total funding: $44.5m
Company HQ
Ventura, Palo Alto, CA
Leadership
Kian Katanforoosh
(Co-Founder & CEO)Alongside Workera, Kian is an Adjunct Lecturer at Stanford University and a Scout at Sequoia Capital. Previously he co-founded Daskit.
Adam Lavezzo
(COO)Prior to joining Workera in 2023, Adam spent nearly 6 years at BetterUp as a VP of Operations/Revenue Strategy & Operations. He also spent 6 years at Bain & Company as a Consultant and Manager.
Tim DaRosa
(CRO)Tim joined Workera in 2024 as CMO, moving into the role of CRO in 2026. Previously, he was the Head of Marketing at Airkit.ai and CMO at Zadara.He also spent over 3 years at HackerOne as VP, then Senior VP of Marketing.
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