Data Scientist

Synaptec Health’s mission is to accelerate the world’s transition to sustainable healthcare.

In pursuit of this mission, we make the world’s only fully automated medical billing and coding technology using the latest in machine learning and natural language processing to code medical charts at scale while exceeding the accuracy and consistency of manual processes.

We are backed by lead Silicon Valley technology investors, including Fusion Fund and Foothill Ventures, founded in San Francisco, California, and have built a diverse and distributed team hailing from all over the globe.

Synaptec Health is looking for world-class talent ready to challenge the hardest problems in data science and machine learning to improve healthcare for both patients and doctors. You’ll learn and be exposed to more technology than any of your previous jobs while receiving valuable early-stage startup equity and working with a team of truly talented and passionate people.

We believe that hard work and innovative solutions result in industry change, so we prioritize hiring top talent and cultivating a culture based on merit–if you've previously done exceptional work, join us to rethink the future of healthcare.

We’re founded in San Francisco, and are looking to hire the best candidates regardless of location. If you’re not in the Bay Area, remote work is fully supported, and we’ll ensure you get to spend as much time as necessary in person to onboard and meet the team face to face.

Role:

Synaptec Health is looking for a Data Scientist to build predictive models and compile actionable insights from large healthcare datasets. You will set best practices for how to use our data to direct developer efforts and will have end-to-end ownership of the design, development and deployment of NLP and ML algorithms given complex healthcare data. We are looking for a driven contributor who is goal-oriented, results-driven, and data-centric with strong hands-on experience in all layers of the data integration and analytics stack.

We are looking for folks that are passionate about understanding data, are well versed in machine learning techniques, and love to build models. A passion for measuring model quality and iteratively improving them using feature engineering is a big plus. You will also be responsible for building frameworks to automate your workflows and enable shorter iteration cycles.

Responsibilities:

• Work with large, complex healthcare data sets and solve non-routine machine learning problems
• Build and own data pipelines to format and store data from source to destination for training and model building
• Utilize strong database skills to filter and cleanse unstructured (or ambiguous) data into usable and analyzable formats
• Collaborate with product management and executive teams to help create actionable, high-impact insights across product lines and functions
• Develop frameworks to test and demonstrate model quality

Requirements:

• Experience with feature engineering and model building, including common classification models, model evaluation, and related statistics
• Ability to program in at least one high-level language (preferably Python)
• Proven problem-solving and debugging skills
• Effective written and verbal communication skills
• Bachelors, Masters, or higher in a quantitative field (such as Engineering, Statistics, Math, Economics, or Computer Science), preferably with work experience of 4+ years

Nice-to-haves:

• Experience with classical NLP techniques and/or large language models
• Experience shipping production code and production ML models
• Exposure to S3, Docker, and orchestration tools (Airflow, etc.)

Job Type: Full-time

Pay: $100,000.00 - $225,000.00 per year

Benefits:

• 401(k)
• Dental insurance
• Flexible schedule
• Health insurance
• Life insurance
• Paid time off
• Retirement plan

Compensation package:

• Bonus opportunities

Experience level:

• 5 years

Schedule:

• Monday to Friday

Education:

• Bachelor's (Required)

Experience:

• Data scientist: 4 years (Required)

Work Location: Remote