Machine Learning Engineer / Data Scientist
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer / Data Scientist based in the United States.
This role sits at the core of building and deploying end-to-end machine learning solutions that directly influence business outcomes. You will work across the full ML lifecycle, from framing ambiguous business problems to delivering production-ready models and monitoring their real-world performance. The position combines hands-on data science, applied machine learning, and strong stakeholder collaboration in a client-facing environment. You will work with complex, real-world datasets to design features, train models, and translate insights into actionable decisions. A key part of the role involves ensuring models are not only accurate but also scalable, explainable, and production-ready. It is a high-impact position where technical depth, communication, and business understanding all play a critical role.
Accountabilities:
- Translate business challenges into machine learning problems such as classification, regression, forecasting, clustering, and anomaly detection.
- Collaborate with stakeholders to define success metrics, constraints, and evaluation strategies aligned with business goals.
- Extract, clean, and analyze data using Python and SQL, ensuring data quality, consistency, and readiness for modeling.
- Design and build robust feature engineering pipelines, including transformations, encoding, scaling, and aggregation logic.
- Develop, tune, and validate machine learning models across supervised, unsupervised, and time series use cases.
- Apply deep learning techniques using frameworks such as PyTorch or TensorFlow/Keras when appropriate.
- Perform model evaluation, error analysis, and interpretability analysis using metrics, SHAP, and cohort-based insights.
- Support deployment efforts by collaborating on APIs or batch pipelines and contributing to MLOps practices such as monitoring and retraining.
- Communicate findings, trade-offs, and recommendations clearly to both technical and non-technical stakeholders.
- Document methodologies, assumptions, and results to ensure reproducibility and transparency.
- 3–8 years of experience in data science, machine learning engineering, or applied ML roles.
- Strong proficiency in Python for data analysis and modeling (pandas, NumPy, scikit-learn or equivalent).
- Advanced SQL skills including joins, window functions, and performance-aware querying.
- Solid foundation in statistics, experimentation, and probabilistic reasoning.
- Hands-on experience with classification, regression, time series forecasting, and clustering techniques.
- Experience with deep learning frameworks such as PyTorch or TensorFlow/Keras.
- Ability to work with messy, ambiguous datasets and translate them into structured ML solutions.
- Strong communication skills with the ability to explain complex results in simple, actionable terms.
- Preferred: experience with Databricks, cloud platforms (AWS/GCP/Azure), orchestration tools (Airflow, Prefect, Dagster), and MLOps workflows.
- Preferred: exposure to production deployment, model monitoring, and retraining pipelines.
- Must be able to work in the United States without immigration sponsorship.
- Opportunity to work on end-to-end machine learning solutions with measurable business impact.
- Remote-friendly work environment with collaboration across distributed teams.
- Exposure to diverse industries and real-world enterprise AI use cases.
- Strong technical growth through hands-on work with modern ML, cloud, and MLOps tools.
- Collaborative, learning-oriented environment with cross-functional stakeholders.
- Opportunity to contribute to production-grade AI systems and scalable ML infrastructure.