At Dot Compliance, we rely on powerfully insightful data to power our SaaS quality management solutions for the life sciences industry. We’re seeking an experienced data scientist to contribute to our mission to deliver AI assisted solutions to our customers. Our ideal team member will have the mathematical and statistical expertise you’d expect, but a natural curiosity and creative mind that’s not so easy to find. As you mine and interpret data, we will rely on you to ask questions, connect the dots, and uncover opportunities that lie hidden within—all with the ultimate goal of realizing the data’s full potential.
- Collaborate with product design and engineering to develop an understanding of needs
- Research and devise innovative statistical and machine learning models
- Communicate findings to all stakeholders
- Keep current with technical and industry developments
- Analyze data for trends and patterns, and Interpret data with a clear objective in mind
- Execute analytical experiments methodically to help solve various problems
- Devise and utilize methods to mine data stores, perform data and error analysis to improve models, and clean and validate data for uniformity and accuracy
- Research and recommend algorithms to address a variety of use cases including natural language processing, recommendation engines, predictive analytics, statistical analysis, data automation, data mining, machine learning and data visualization.
- Work closely with the product and engineering teams to strategize and execute the development of products
- Bachelor’s degree in statistics, applied mathematics, or related discipline
- 5+ years’ experience in data science
- Proficiency with data mining, mathematics, and statistical analysis
- Advanced pattern recognition and predictive modeling experience
- Comfort working in a dynamic, research-oriented group with several ongoing concurrent projects
- Experience using statistical computer languages (R, Python, SLQ, etc.)
- Experience with common AI/ML packages including PyTorch, and Tensorflow
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
- Understanding of model evaluation & scoring including avoidance of model bias.
- Experience planning and executing the full lifecycle of machine learning models from requirements gathering, exploratory data analysis, visualizations, model training, model evaluation, and model deployment and monitoring.
- Experience working in Pharmaceutical or Medical device industries is an advantage