Machine Learning Engineer
This is a REMOTE Position
PURPOSE AND SCOPE:
The Machine Learning engineer will design and implement the processes and technology to accelerate Fresenius’ time to insights and innovate the way we improve our patient’s lives through artificial intelligence. This individual will leverage cutting edge technologies and processes to bring efficiencies to the AI system development lifecycle and ensure it’s scalability to meet the needs of our global patient population.
Supports FMCNA's mission, vision, core values and customer service philosophy. Adheres to the FMCNA Compliance Program, including following all regulatory and division/company policy requirements.
MACHINE LEARNING FOCUS:
- The Machine Learning Engineer position lives in the cutting edge of Data Science and Information Technology.
- They will collaborate with other data management, medical and analytics experts to derive insights value from data, create models, collaborate with other Data Science experts to implement and maintain their models at scale and drive the evolution of technology to support precision medicine and personalized care for our patients.
- The ML Engineer will develop fully automated AI/ML systems integrated with enterprise healthcare systems where insights will turn into actions loop back into features that will drive more insightful models.
- The position is responsible for managing the activities of analysts and developers delivering enterprise data warehouse and reporting solutions.
- Working closely with FMC clinical, business and IT colleagues, this experienced development manager is responsible for project management, analysis, design, development and on-going support for assigned programs and projects.
- This manager is also a key member of the architecture and design team for enterprise warehousing and reporting initiatives.
PRINCIPAL DUTIES AND RESPONSIBILITIES:
Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress Managing available resources such as hardware, data, and personnel so that deadlines are met
Collaborating with Information Technology and Data Science teams to build processes and technology to enable the implementation and management of enterprise scale models
•Designing and building MLOps capabilities to monitor and automate management of models
Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
•Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
Verifying data quality, and/or ensuring it via data cleaning
Supervising the data acquisition process if more data is needed
Finding available datasets online that could be used for training
Defining validation strategies
Defining the preprocessing or feature engineering to be done on a given dataset
Defining data augmentation pipelines
Training models and tuning their hyperparameters
Analyzing the errors of the model and designing strategies to overcome them
Deploying models to production Develops, mentors and leads assigned team members. Oversees and assesses development
Contributes to the definition and timely achievement of overall project goals. Makes recommendations, and when appropriate, acts independently to resolve scientific and technical problems encountered to improve productivity of results. Ensures activities are consistent with the project critical path and responds appropriately to changing priorities.
Presents findings at international conferences and locally at group and departmental meetings.
Ability to work on issues that impact or address future concepts, products or technologies.
Ability to network with key decision makers.
Performs other related duties as assigned.
PHYSICAL DEMANDS AND WORKING CONDITIONS:
- The physical demands and work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Little to no travel is required for this role - occasional travel to conferences.
- Advanced Degree in computer science, epidemiology, public health, statistics, data analysis or similar preferred.
- Working knowledge in Python, SAS, and Linux at a minimum.
EXPERIENCE AND REQUIRED SKILLS:
- 4-5 years' related experience; or a Master's degree with 2 years' experience.
- 2 or more years training and managing ML models
- Strong experience using SAS Base, Python or other equivalent statistical application
- Strong technical skills in machine learning, deep learning, natural language processing, or artificial intelligence.
- A passion for making these methods robust and scalable.
- Experience with APIs, version control software (git), documentation, containerization (Kubernetes/Docker)
- Ability to explain and present analyses and machine learning concepts to a broad technical and nontechnical audience
- Strong programming skills.
- Excellent verbal and written communication skills required for interacting with physicians, nurses, management, and peers.
- Ability to clearly summarize methodology and key points of program/report in technical documentation/specifications is also required.
- Strong knowledge of outcomes research and analysis of health quality data.
- Ability to work independently without day-to-day supervision or the use of templates.
EO/AA Employer: Minorities/Females/Veterans/Disability/Sexual Orientation/Gender Identity
Fresenius Medical Care North America maintains a drug-free workplace in accordance with applicable federal and state laws.