Applied Data Scientist / Machine Learning Engineer – Decision Intelligence

WorkWave

Applied Data Scientist or Machine Learning Engineer developing ML-powered products for decision intelligence at WorkWave. Driving model development and integration with customer-facing SaaS products for enhanced business operations.

Posted 6/22/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $160,000 - $170,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSAzureBigQueryCloudGoogle Cloud PlatformPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Drive the development of machine learning capabilities (forecasting, recommendation, ranking, optimization, or decision intelligence) powering customer-facing SaaS products.
Design reliable data and feature pipelines alongside models from discovery through experimentation, validation, deployment, and monitoring.
Partner with Product Managers and Software Engineers to embed ML directly into product workflows, user experiences, and decision-making tools.
Move quickly from prototype to production while balancing accuracy, interpretability, latency, maintainability, and business impact.
Define offline and online evaluation strategies, including model quality, drift, and reliability. Design A/B tests and causal measurement frameworks to prove ML features improve customer outcomes.
Collaborate with Data teams to ensure models are supported by high-quality features, while building feedback loops so product experiences improve over time.
Help manage and optimize cloud data infrastructure, ensuring trustworthy insights and proactively managing data health before it impacts users.
Bring strong judgment around when to use traditional ML, statistical modeling, LLMs, heuristics, or simpler product logic. Make practical trade-offs across model complexity and customer impact.
Clearly communicate what ML can and cannot solve to influence roadmap decisions, helping identify where machine learning can create true product differentiation.
Guide and mentor other data scientists, ML engineers, analysts, and cross-functional partners in applied ML best practices.

Requirements

What you’ll need

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.
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.
Strong understanding of supervised learning, forecasting, ranking, recommendation systems, optimization, or statistical modeling. Experience with real-world, imperfect product datasets is essential.
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).
Hands-on experience operating within cloud environments (AWS, GCP, or Azure).
Excellent communication skills with the ability to explain complex technical trade-offs clearly to product, engineering, and non-technical business stakeholders.

Benefits

Comp & perks

Employees can expect a robust benefits package, including health and dental and 401k with company match
Find your perfect work/life balance with our Flexible Time Off policy or generous PTO plan (role dependent) and paid holidays
Up to 4 weeks paid bonding leave
Tuition reimbursement
Robust Employee Assistance Program through TotalCare offering free counseling 24/7/365, plus financial counseling, legal guidance, adoption assistance services and much more!
24/7 access to virtual medical care with Teladoc
Quarterly awards based on peer nominations
Regional discounts and perks
Opportunities to participate in charitable events and give back to the community

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningdata sciencePythonSQLScikit-LearnXGBoostPyTorchTensorFlowsupervised learningstatistical modeling

Soft Skills

communicationmentoringcollaborationjudgmentinfluence