Anomaly is a healthcare technology company with a bold mission: to bring Precision Payments to healthcare.
Fraud, waste, and abuse costs up to 10% of healthcare spend, over $300 billion every year. Anomaly brings together deep domain experts, top engineers and data scientists, and experienced payer and benefits leaders to tackle this challenge. We're building software that uses analytics, ML, and proprietary data to catch and prevent improper payments. Our team includes alumni from Apple, Google, Foursquare, Optum, Cotiviti and other leading tech and healthcare companies.
Anomaly is backed by Redesign Health, a leading healthcare innovation platform.
About the Applied Machine Learning Engineer Role
As our Applied Machine Learning Engineer, you will collaborate with other data scientists, as well as members of the platform engineering team in order to execute cutting-edge machine learning techniques to find fraud, waste, and abuse in healthcare data. Your goal is to train and test models on millions of data-points, and help build-out a state-of-the-art human-in-the-loop ML infrastructure.
In this role, you will report directly to the Head of Data Science at our New York City headquarters. This role will remote until it is safe to return to the office. You will collaborate with other data scientists, as well as members of the platform engineering team. You will also engage in daily interactions with domain experts on the payment integrity team.What you'll do:
- Write production-level machine learning models
- Executing cutting-edge machine learning techniques to find fraud, waste, and abuse in healthcare data.
- Making infrastructural choices in order to ensure that the trained models scale well across millions of health insurance claims
- Collaborate with the team to build internal tools to support our company mission
- Working with domain experts in the health insurance field to incorporate their knowledge into trained models.
- Building interactive internal tools to obtain expert feedback for the purposes of model validation.
- Have strong impact on innovative engineering projects
- Running unsupervised anomaly detection on millions of heterogeneous health-insurance features using PySpark.
- Shining a bright light on black-box models to make all anomalous signals easily interpretable.
- Interacting with domain experts and encoding their experience into simple heuristics. Pushing these heuristics to production for the purposes of weak supervision.
- Training large-scale, weakly-supervised ML models using tools like Snorkel. Making sure these models are interpretable.
- Tweaking models using expert feedback. Building dashboards to solicit that feedback using human-in-the-loop training techniques.
- Experimenting with transformer model applications using the Pytorch / Tensorflow libraries.
- Standard knowledge of Python data-science stack
- 5+ years in software engineering experience
- Hacker mindset
- Experience building out production-ready machine learning solutions
- Communicates Effectively: Attentively listens to others. Adjusts to fit the audience and the message. Provides timely and helpful information to others across the organization. Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences.
- Manages Ambiguity: Deals comfortably with the uncertainty of change. Effectively handles risk. Can decide and act without having the total picture. Is calm, and productive, even when things are up in the air.
- Courage: Readily tackles tough assignments. Faces difficult issues and supports others who do the same. Provides direct and actionable feedback.
- Instills Trust: Follows through on commitments. Is seen as direct and truthful. Keeps confidences. Practices what he/she preaches. Shows consistency between words and actions. Gaining the confidence and trust of others through honesty, integrity, and authenticity
- Action Oriented: Readily takes action on challenges, without unnecessary planning. Identifies and seizes new opportunities. Displays a can-do attitude in good and bad times. Steps up to handle tough issues.
- Nice To Have
- Exposure to PySpark, PyTorch, Snorkel
- Experience working in a fast-paced, innovative environment
Headquartered in NYC with a strong remote team, Anomaly brings together a diverse group deeply committed to our mission to bring Precision Payments to healthcare. Data scientists, engineers, clinicians, and more work together to realize a future where healthcare payments flow with precision. We live by our values, which help us accomplish more and create a workplace we love.
- Move with urgency: Our customers needed our product yesterday. We move fast—but not too fast—to bring our solution to market
- Be willing to sit on the floor: No one is too senior to jump in and get the job done
- Think 10x: We celebrate incremental improvements, but we always look for the step-function win
- Do the right thing, even when it's hard: We put privacy and security first, and treat all data with the care like it’s our own
- Seek feedback at 30%: We ask for feedback uncomfortably early, and offer it proactively (and respectfully) to others
- Default to open: We internally share everything we can, creating a culture of trust and empowerment
- Be kind: We take our work seriously, but we’re never too busy to be kind