We are looking for a product-minded Applied Data Scientist or Machine Learning Engineer to help build, ship, and scale ML-powered products that directly improve how our customers make decisions, operate their businesses, and serve their own users.

This is not a research-only role, nor is it a service-oriented internal analytics position. We want someone who has taken machine learning from problem definition through experimentation, production deployment, measurement, iteration, and long-term ownership. You understand that great models are not just accurate in notebooks—they are usable, explainable, measurable, scalable, and valuable inside a real product.

Whether your background leans heavily toward Data Engineering/ML Ops or Applied Data Science, you have a strong bias toward shipping and an interest in bridging both worlds to bring AI to life.

WHAT YOU'LL DO:

WHO YOU ARE:

WHAT YOU’LL BRING:

• Experience: 3+ years (ideally 5+) of professional experience in applied data science, machine learning, or ML engineering, including hands-on experience building and shipping models into production products. Experience with SaaS products is highly valued.

• Technical Core: Strong Python skills and hands-on experience with applied ML libraries and frameworks (e.g., Scikit-Learn, XGBoost, PyTorch, TensorFlow). Solid SQL expertise is required.

• ML & Modeling Depth: Strong understanding of supervised learning, forecasting, ranking, recommendation systems, optimization, or statistical modeling. Experience with real-world, imperfect product datasets is essential.

• Ops & Orchestration: Familiarity with MLOps concepts (model versioning, feature pipelines, orchestration via Airflow/dbt/Dagster, monitoring, drift detection) and modern data platforms (e.g., Snowflake, BigQuery, Redshift, Databricks).

• Cloud Infrastructure: Hands-on experience operating within cloud environments (AWS, GCP, or Azure).

• Communication & Collaboration: Excellent communication skills with the ability to explain complex technical trade-offs clearly to product, engineering, and non-technical business stakeholders.

BONUS POINTS FOR:

• Experience with decision intelligence, forecasting, customer behavior modeling, workforce/route optimization, or operational intelligence products.

• Experience with LLMs, GenAI, or agentic workflows applied to real product use cases.

• Prior experience acting as a Senior or Lead scientist responsible for guiding technical direction.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.