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 Tensor Flow/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, Num Py, 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 Tensor Flow/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.

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