Data Scientist – Machine Learning Engineer
Title: Data Scientist – Machine Learning Engineer
Location: United States
We are looking for an experienced Data Scientist/Machine Learning Engineer with a strong blend of business acumen and technical skills. The Data Scientist/Machine Learning Engineer is involved in the full lifecycle of data solutions, from data engineering, modeling, operationalizing, presentation, maintenance, and benefit tracking. It is the ideal opportunity to become part of an innovative and energetic team that develops analytical tools that influence both our products and our customers.
- A strong team player who will collaborate with product management, product owners and engineering departments to understand business needs and devise solutions
- The role of the Data Scientist/Machine Learning Engineer is a highly collaborative role. This position is expected to work closely with other analytics, data warehousing, and data engineering teams in creating big data applications through the utilization of structured and unstructured data, designing optimal data architecture, and experimenting on new machine learning techniques.
- The Data Scientist/Machine Learning Engineer also works in collaboration with senior management of Data and Analytics and serves as a reliable advisor in the creation and implementation of useful information for the business
- Participate in the full life cycle end-to-end solutions, from planning, designing, technical implementation, troubleshooting, deployment, validation and maintenance
- Use healthcare analytics background on disparate clinical data sources to create unified data models to solve business needs
- Create machine learning models (from structured, unstructured, imaging and time series healthcare data) and applications that will drive operational improvements across the healthcare continuum of care and improve insights
- Design, implement, and maintain deep learning and machine learning models to the cloud (e.g. AWS, GCP, Azure. Preferably AWS.)
- Design, test and maintain Natural Language Processing (NLP) applications using the latest in testing methodologies
- Collaborate with other Data Scientists/Machine Learning Engineers and provide technical direction
- Perform code reviews to guarantee high quality products moving to production
- Develop creative solutions for diverse problems including unstructured data, ontology development, and machine learning applications
- Evaluate new technologies and tools prior to wider business adoption
- Broad examples of innovative solutions this may could be part of include: Genomics, Natural Language Processing (NLP), Medical Imaging, Geo-spatial analytics, and AI/Machine Learning
- The candidate is tasked with maintaining a deep understanding the business’s marketplace dynamics. The Data Scientist/Machine Learning Engineer takes initiative and conducts exploratory data analyses and experimental designs, which will help the business to better understand trends and behavior within these markets and settle on the most suitable strategies to drive success and achievements of goals and targets.
- 4+ years of healthcare industry experience in data science/machine learning roles
- Experience with visualization tools such as Tableau
- Experience using tools in at least one cloud computing platform such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform
- Proficient with relational databases and programming languages, such as SQL, Python, scikit-learn, TensorFlow, and shell scripting
- Deep understanding of various machine learning algorithms, including supervised, unsupervised, and deep learning algorithms
- Experience with machine learning and computational statistics packages
- Strong analytical skills, business and product instincts
- Excellent verbal communications, including the ability to articulate complex concepts to both technical and non-technical audiences
- Researching and converting those results into applications and features that drive business impact
- Knowledge on NoSQL databases such as MongoDB a plus, but not required
- Passion for teaching and mentoring
- A degree in a quantitative field such as Statistics, Machine Learning, Mathematics, Computer Science, Economics, Epidemiology or any other related field is required. A graduate-level degree is preferable.
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