Applied Data Scientist / Machine Learning Engineer – Decision Intelligence

This is a fully remote position, open to applicants in United States.

📋 Description

• Lead the advancement of machine learning capabilities (such as forecasting, recommendation, ranking, optimization, or decision intelligence) that enhance customer-facing SaaS products.

• Develop dependable data and feature pipelines in conjunction with models from initial discovery through experimentation, validation, deployment, and monitoring.

• Collaborate with Product Managers and Software Engineers to seamlessly integrate ML into product workflows, user experiences, and decision-making tools.

• Progress swiftly from prototype to production while maintaining a balance between accuracy, interpretability, latency, maintainability, and business impact.

• Establish both offline and online evaluation strategies, which include model quality, drift, and reliability. Design A/B tests and causal measurement frameworks to validate that ML features enhance customer outcomes.

• Work in partnership with Data teams to ensure models are backed by high-quality features, while creating feedback loops to continuously enhance product experiences.

• Assist in managing and optimizing cloud data infrastructure, guaranteeing trustworthy insights and proactively addressing data health to prevent user impact.

• Apply sound judgment in deciding when to utilize traditional ML, statistical modeling, LLMs, heuristics, or simpler product logic. Make pragmatic trade-offs between model complexity and customer impact.

• Effectively communicate the capabilities and limitations of ML to influence roadmap decisions, aiding in the identification of areas where machine learning can provide significant product differentiation.

• Mentor and guide other data scientists, ML engineers, analysts, and cross-functional partners in best practices for applied ML.

⛳️ Requirements

• Over 3 years (preferably 5+) of professional experience in applied data science, machine learning, or ML engineering, including direct experience in building and deploying models into production products. Experience with SaaS products is highly regarded.

• Proficient in Python with practical experience using applied ML libraries and frameworks (e.g., Scikit-Learn, XGBoost, PyTorch, TensorFlow). Strong SQL skills are essential.

• Comprehensive understanding of supervised learning, forecasting, ranking, recommendation systems, optimization, or statistical modeling. Experience with real-world, imperfect product datasets is crucial.

• Familiarity with MLOps concepts (model versioning, feature pipelines, orchestration using Airflow/dbt/Dagster, monitoring, drift detection) and contemporary data platforms (e.g., Snowflake, BigQuery, Redshift, Databricks).

• Practical experience working within cloud environments (AWS, GCP, or Azure).

• Exceptional communication skills with the ability to articulate complex technical trade-offs to product, engineering, and non-technical business stakeholders clearly.

🏝️ Benefits

• Employees can expect a comprehensive benefits package that includes health and dental coverage as well as a 401k with company matching.

• Achieve your ideal work/life balance with our Flexible Time Off policy or generous PTO plan (depending on the role) and paid holidays.

• Up to 4 weeks of paid bonding leave.

• Tuition reimbursement available.

• Extensive Employee Assistance Program through TotalCare, offering free counseling 24/7/365, along with financial counseling, legal guidance, adoption assistance services, and more!

• 24/7 access to virtual medical care via Teladoc.

• Quarterly awards based on peer nominations.

• Regional discounts and perks.

• Opportunities to engage in charitable events and give back to the community.