Applied Data Scientist / Machine Learning Engineer (Decision Intelligence)
New
Remote, USFull-TimeSenior
Salary$160,000 - $170,000 a year plus a 10% annual bonus
Apply NowOpens the employer's application page
Job Details
- Experience
- 3+ years (ideally 5+)
- Required Skills
- PythonSQLPyTorchSnowflakeAirflowTensorflowdbtscikit-learnMLOps
Requirements
- 3+ years (ideally 5+) of professional experience in applied data science, machine learning, or ML engineering.
- Hands-on experience building and shipping models into production products.
- Strong Python skills and hands-on experience with applied ML libraries (e.g., Scikit-Learn, XGBoost, PyTorch, TensorFlow).
- Solid SQL expertise.
- Understanding of supervised learning, forecasting, ranking, recommendation systems, optimization, or statistical modeling.
- Familiarity with MLOps concepts (model versioning, feature pipelines, orchestration, monitoring, drift detection).
- Experience with orchestration tools (e.g., Airflow, dbt, Dagster).
- Experience with 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 to diverse stakeholders.
Responsibilities
- 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.
- Collaborate with Data teams to ensure models are supported by high-quality features and build feedback loops.
- Help manage and optimize cloud data infrastructure and proactively manage data health.
- Communicate ML capabilities to influence roadmap decisions and provide technical mentorship to the team.
View Full Description & ApplyYou'll be redirected to the employer's site
