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Senior Data Scientist

Envisagenics

Envisagenics

Data Science
New York, NY, USA
Posted on Monday, November 4, 2019

Senior Data Scientist

New York, NY

Description

We are looking for a highly analytical and innovation-driven Senior Data Scientist to lead the department and craft a bold vision around the intersection of machine learning, high performance computing, and big data in the setting of biology and genomics. Your work in predictive analytics and big data at Envisagenics will help save lives and deliver cures to people faster than ever before. You will leave work every day knowing that your work will make a huge difference to patients around the world, and you will help grow the data scientist team.

Envisagenics is already widely recognized as a leader in RNA therapeutics, with investors such as Microsoft and Breakout Labs. As a member of a start-up company, you should be excited about shaping the team and participating in multiple roles. You should also be pro-active in your contributions and ready to evolve as the team grows.

Responsibilities

  • Develop algorithms and machine learning applications for RNA-seq analysis and disease gene prioritization that will be integrated in Envisagenics’ drug discovery platform, SpliceCore®.
  • Research and develop statistical learning models for data analysis while managing and supervising these efforts from junior members of the team.
  • Ensure that the company stays up to date with technological advancements.
  • Collaborate with product management, bioinformatic scientist, and engineering teams to better understand the company’s needs and devise solutions.
  • Apply statistical data analysis to support assay development experiments and guide experimental approaches.
  • Identify publicly available biological datasets for building relevant models

Qualifications

  • MS or Ph.D. in a quantitative discipline (i.e., computer science, statistics, mathematics, bioinformatics, or related field).
  • 3+ years of experience in the analysis and application of algorithms and models to large scale data problems in biology, genomics, transcriptomics, or proteomics including applied experience with machine learning.
  • Significant familiarity and comfort with high-performance computing (HPC) capabilities using computer clusters, Slurm Workload Manager, Hadoop, and Spark.
  • Demonstrated experience building machine learning algorithms and data products.
  • Experience translating technical or statistical analysis results into business recommendations—and being able to discuss those recommendations in plain English to smart non-technologists in various roles.
  • Experience with C++, perl, python, R, or other scripting languages.
  • Experience working in a parallel environment and writing bash/shell scripts.
  • Significant experience with machine learning, especially random forest, supported vector machines, logistic regression, lasso regression, and Bayesian probability.
  • Ability to work in a team-oriented, Agile environment.
  • Must have proficient written, communication and presentation skills.

Preferred Skills

  • Experience working with large data sets
  • Experience with splicing regulation.
  • Data visualization proficiency
  • Cloud computing experience (e.g., Microsoft Azure, Google Cloud, Amazon AWS)

About Us

Envisagenics is a bioinformatics startup developing cloud-based solutions to leverage biomedical big data and accelerate R&D therapeutics through machine learning. Envisagenics is a spin-off from Cold Spring Harbor Laboratory, a biomedical research institute ranked #1 worldwide for scientific impact in the field of genetics. Envisagenics’ core technology, SpliceCore® was developed by our CTO while at Cold Spring Harbor Laboratory in Dr. Adrian Krainer’s Laboratory, a leader in the RNA splicing field. The mission of Envisagenics is to materially advance the world’s ability to cure diseases, discovering new and more efficient drug targets through the analysis and integration of genomic data alongside our pharma partners.

Envisagenics evaluates qualified applicants without regard to race, color, national origin, religion, sex, sexual orientation, gender identity, disability, protected veteran status, and other statuses protected by law.