Senior Machine Learning/Data Engineer

PsychPlus is on a mission to transform mental health care delivery by offering a digital-first, modern approach to psychiatry and therapy. With both in-person locations across Texas and virtual care options, we strive to make mental health services accessible and convenient. Our integrated model combines exceptional medical and psychological care with cutting-edge technology to meet the diverse needs of our patients. Join us in ensuring affordable, accessible mental health care for everyone.

Key Responsibilities
You will take on a hands-on role as a Senior Machine Learning/Data Engineer within our Data/AI/ML team, responsible for building, deploying, and maintaining machine learning models that impact clinical and operational outcomes. This role uniquely blends applied machine learning, production engineering, and data analytics. You will develop models using real-world clinical and operational data, deploy them into production, and ensure their ongoing performance through monitoring and retraining processes. Additionally, you will build backend services and APIs that integrate model predictions with our electronic health records (EHR) system, writing production-grade, tested, and version-controlled code.

A critical part of your work involves deep data exploration—understanding the nuances and inconsistencies in EHR and claims data, documenting findings to accelerate future model development. You will design and implement key business metrics with defensible definitions and productionize them as scheduled jobs. Your analytical skills will be used to answer operational questions and clearly communicate insights to both technical and non-technical stakeholders.

Secondary responsibilities may include creating dashboards for various audiences, designing and analyzing controlled rollouts and A/B tests to measure model impact, and contributing to applied research in areas such as digital phenotyping and clinical trajectory modeling alongside a team with a strong publication and patent record.

Required Qualifications
- Fluent in English, both written and spoken, to collaborate effectively with colleagues across Europe and the U.S.
- At least 6 years of professional experience building machine learning models, with a minimum of 3 years deploying and operating models in production on live data.
- Strong expertise in gradient boosting and tree-based methods, feature engineering on tabular and temporal data, handling class imbalance, probability calibration, and setting operating thresholds under business constraints. Deep learning experience is a plus but not the focus.
- Expert-level Python programming skills for production environments, including modular, tested, and version-controlled code, along with advanced PySpark capabilities.
- Proficiency in SQL, including complex joins, window functions, and query optimization on large datasets.
- Proven experience in backend engineering, building and maintaining APIs (REST or GraphQL) that serve predictions in production, with ownership of the entire service lifecycle.
- Minimum 3 years working with modern cloud data platforms, preferably Microsoft Fabric or Azure; substantial experience with Databricks, AWS, or GCP is also considered.
- Hands-on experience with MLOps tools and practices such as experiment tracking, model versioning, reproducible training pipelines, and production monitoring (e.g., MLflow).
- Demonstrated ability to work with messy, poorly documented, and inconsistent real-world data, reverse-engineering source system behavior and identifying silent data-quality issues.

Preferred Qualifications and Benefits
- Experience with healthcare data, including EHR, claims, billing, or clinical records, and familiarity with HIPAA or similar data protection regulations.
- Skills in dashboarding tools like Power BI, including ownership of semantic models.
- Knowledge of survival analysis, uplift modeling, causal inference, or optimization under constraints.

Soft Skills
Intellectual honesty is essential—you will report findings transparently, even when data challenges expectations. Strong communication skills are required to explain technical decisions clearly and tailor explanations to diverse audiences. A growth mindset is valued; you enjoy debugging and view challenges as opportunities to improve. Finally, being a team player who collaborates effectively and raises engineering standards around you is crucial.

Join PsychPlus to make a meaningful impact on mental health care through innovative data science and engineering.