Data Scientist

Requirements

• Strong applied data science experience (roughly 4+ years), with a track record of shipping models that made it into a real product
• Explicit jobs-and-skills or workforce data experience, OR experience with closely related data where there is a clear pathway to apply it to workforce problems (this is a firm criterion for the role)
• Fluency in Python and SQL, and solid grounding in machine learning, NLP, and recommendation/matching techniques
• Comfort working with large, imperfect datasets and making sound judgment calls about them
• Clear communication: you can explain a model and its tradeoffs to a non-technical audience
• (Desirable) Experience with recommender systems, ranking, or search at scale
• (Desirable) Familiarity with skills/occupation frameworks (e.g. O*NET, ESCO) or HR/labor market data
• (Desirable) Experience pairing classical ML with LLMs, including where to use each and how to add guardrails
• (Desirable) Publications, presentations, blog posts, or other public artifacts showcasing your expertise and depth of knowledge in data science

What the job involves

• We're seeking a Data Scientist to join our team
• You will build the models at the heart of our product: the systems that connect people to the right jobs, skills, and pathways
• This is hands-on, applied data science on real workforce problems, working with skills and occupation taxonomies, labor market data, and the matching and recommendation systems that turn that data into better outcomes for job seekers
• You will partner closely with Engineering, Product, and our Director of Data & AI to take models from idea to production
• Applied modelling: Build, evaluate, and ship models for matching, recommendation, and ranking that directly shape the job seeker experience
• Skills and jobs data: Work with skills, occupation, and career taxonomies and labor market data, improving how we represent and reason about the world of work
• Production partnership: Collaborate with Engineering to move models into production reliably, and monitor and improve them once they are live
• Clear analysis: Translate messy, real-world data into clear findings and recommendations that the team and our customers can act on
• Languages: Python, SQL
• Machine learning and NLP: scikit-learn, modern NLP and embedding tooling, AWS SageMaker
• Data orchestration and transformation: Airflow, dbt
• Data storage and warehousing: PostgreSQL, Redshift, MongoDB
• Visualization and reporting: Looker