Data Science Engineer
Who we are
At PrognomiQ, we aim to develop and commercialize transformative tests for early detection and treatment of cancer and other complex diseases. To accomplish this, we are leveraging leading proteomics technology, including the Proteograph product suite from Seer Bio, in combination with other multi-omics assays to develop unprecedented molecular views of health and disease. We are a team of experienced and accomplished scientists supported by a group of leading investors in healthcare and technology.
We are seeking a versatile and experienced Data Scientist who also possesses a strong background in Data Engineering. In this hybrid role, you will leverage your expertise in data engineering to optimize data infrastructure while using your data science skills to analyze large molecular datasets and train machine learning models.
Would you like to be at the leading edge of multi-omics data generation and analyses that changes how cancer and other complex diseases are detected and treated? Or are you looking to bring innovative and disruptive solutions to market? If you answer “yes” to either of these questions, let us know by applying!
Description of role
- Optimize and maintain scalable, efficient, and reliable data pipelines for the collection, storage, and processing of data from various sources.
- Collaborate with data analysts, data scientists, and other stakeholders to understand data requirements and integrate data from multiple sources into a unified data warehouse
- Develop and implement data transformation processes, including data cleansing, normalization, and enrichment, to ensure data quality and consistency.
- Maintain ELT (Extract, Load, Transform) processes to move and transform data efficiently.
- Present findings and insights in a clear and understandable manner to both technical and non-technical audiences.
- Collect, clean, and preprocess large datasets from various sources to ensure data quality and suitability for analysis.
- Apply statistical techniques and mathematical modeling to analyze data and uncover patterns, trends, and correlations.
- Develop and implement machine learning models to solve specific business problems, such as predictive modeling, classification, etc.
- Identify and engineer relevant features from raw data to improve model performance.
- Evaluate the performance of machine learning models, fine-tune them, and deploy them into production environments.
- MS/Ph.D. in Computer Science, Bioinformatics, Statistics, Applied Mathematics, Computational Biology, Physics. Bachelor’s degree in a quantitative field with equivalent job experience will also be considered
- Excellent communications and presentation skills, as well as a desire to work across functional teams
- Excellent interpersonal and communication skills, including business writing and presentations. Ability to communicate objectives, plans, status and results clearly, focusing on critical few key points. Demonstrated ability to work in a matrixed environment, ability to influence at all levels, and build strong relationships.
- Experience with at least one major data warehousing platform (Snowflake, Redshift, Big Query, Spark)
- Strong database fundamentals including SQL, performance and schema design.
- Proficiency in programming languages such as Python or R, and experience with data analysis and machine learning libraries (e.g., Pandas, NumPy, SciPy, Scikit-Learn, TensorFlow, PyTorch).
- Experience with AWS services, including S3, DynamoDB, EC2, Athena and Cloud formation, Terraform.
- Experience with containerization and orchestration tools like Docker
Equal Opportunity Employment
PrognomiQ is an equal opportunity employer that celebrates diversity and inclusion in the workplace. We prohibit discrimination and harassment of any kind based on race, color, sex, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other characteristic protected by law. This policy applies to all employment practices within our organization, including hiring, recruiting, promotion, termination, layoff, recall, leave of absence, compensation, benefits, training, and apprenticeship.