About The Role

You will join a strategic healthcare technology engagement supporting a global medical technology enterprise.

You will own work from problem formulation and experimentation through production implementation, collaborating with data engineers, AI specialists, and MLOps engineers to build reliable, explainable, and scalable solutions on AWS.

Responsibilities

• Lead the scientific design and implementation of forecasting and optimization solutions for medication inventory management
• Develop medication diversion detection models using behavioral analytics, anomaly detection, risk scoring, and explainable ML techniques
• Translate complex clinical and operational needs into clearly defined ML problems with measurable success criteria
• Apply classical statistical methods, mathematical optimization, machine learning, and deep learning to forecasting and decision-making challenges
• Design rigorous experiments, including temporally appropriate data splits, baselines, ablation studies, calibration, and uncertainty quantification
• Build production-ready training pipelines, inference services, batch workloads, and data integrations using AWS-native services
• Collaborate with data engineers on feature engineering, dataset preparation, and reproducible data pipelines
• Partner with MLOps engineers on deployment, monitoring, drift detection, continuous evaluation, and rollback
• Write clean, modular, well-tested Python code and contribute to engineering standards, code reviews, and CI/CD practices
• Use AI-assisted coding tools responsibly, validating generated code for correctness, security, and licensing compliance
• Document methodologies, assumptions, validation results, architecture decisions, runbooks, and model cards in Confluence
• Mentor engineers and junior scientists, communicating technical findings to engineering, clinical, and executive stakeholders
  
  
  

Requirements

• A master’s degree in Machine Learning, Statistics, Operations Research, Applied Mathematics, Computer Science, or a related field; a PhD is an advantage
• At least 5 years of experience in ML or software engineering, including hands-on modeling and ownership of production ML solutions
• At least 3 years of experience building ML services on AWS using native infrastructure and services rather than SageMaker as the primary ML platform
• Deep expertise in time-series analysis and forecasting, including classical, machine learning, deep learning, and hierarchical or multi-series approaches
• Strong knowledge of mathematical optimization, including linear, mixed-integer, convex, and stochastic methods, and their integration with ML predictions
• Excellent Python skills and practical experience with NumPy, pandas, scikit-learn, statsmodels, and PyTorch or JAX
• Experience with forecasting frameworks such as Prophet, GluonTS, or Nixtla
• Strong AWS expertise, including EKS, S3, IAM, Lambda, EventBridge, ECR, CloudWatch, and Step Functions
• Practical experience with Docker, Kubernetes, GitHub Actions, and CI/CD, supported by strong testing, packaging, and observability practices
• A proven ability to move models from research prototypes into reliable production services
• Experience working in Agile/Scrum teams and maintaining technical documentation in Confluence
• Strong written and spoken English, with the ability to explain complex methodologies to technical and business audiences
• Experience in healthcare, pharmacy, supply chain, or regulated environments is an advantage
• Familiarity with causal inference, counterfactual evaluation, time-series foundation models, experiment tracking, model registries, or inference optimization is an advantage
  
  
  

SoftServe is an equal opportunity employer. Qualified applicants will receive consideration regardless of race, color, ancestry, ethnicity, national origin, religion, sex, sexual orientation, gender identity or expression, age, citizenship, disability, health condition, marital or family status, veteran status, or any other characteristic protected by applicable law.