Product-Focused Data Scientist — End-to-End ML

WorkWave; United States; Remote; USD 100,000 - 147,000; Full time

Benefits offered by this job

Competitive pension scheme

Generous holiday allowance

Enhanced parental leave

Ongoing learning resources

Employee Assistance Programme (EAP)

Job summary

WorkWave seeks a product-minded applied data scientist or engineer to transform raw operational data into customer-facing products that drive decisions and business outcomes. You own the process from problem framing to deployment, ensuring models are usable, explainable, and measurable in a real product context.

You will build data pipelines, work with Snowflake/dbt on AWS, and collaborate with Product and Engineering to embed ML within workflows.

Qualifications

• 5+ years in applied data science, ML engineering, or data engineering with production models.
• Strong Python and tabular ML libraries; solid SQL knowledge.
• Experience with dbt and Snowflake modelling, deployment, monitoring.
• Expertise in forecasting, ranking, recommendation, or optimisation.
• Leadership experience guiding technical direction on real products.
• Proven ability to quantify business impact of a shipped model.

Responsibilities

• Own the outcome from ambiguous customer problems to measurable value.
• Learn the domain semantics of customer operations (routes, crews, service history).
• Build required data in Snowflake/dbt and feature pipelines.
• Define how predictions reach customers and report impact with numbers.
• Design offline/online evaluation, A/B tests, or causal analyses to prove value.
• Deliver, deploy, monitor, and manage drift in production models.
• Embed ML in product workflows with Product and Engineering teams.

Skills

Python

SQL

ML engineering

Forecasting

Leadership

Communication

Agentic AI

Tools

Snowflake

dbt

XGBoost

LightGBM

scikit-learn

AWS

Prophet

Kubernetes

Job description

WorkWave seeks a product-minded applied data scientist or engineer to transform raw operational data into customer-facing products that drive decisions and business outcomes. You own the process from problem framing to deployment, ensuring models are usable, explainable, and measurable in a real product context.

You will build data pipelines, work with Snowflake/dbt on AWS, and collaborate with Product and Engineering to embed ML within workflows.