Senior Data Scientist
- Austin, TX
Senior Data Scientist
Why YOU want this position
Since our founding as a groundbreaking provider of oil & gas data, we have evolved our solutions to cover oil & gas analytics, trading & risk, and business automation for customers across the energy industry. Enverus represents this growth while bringing us closer together as one team. Enverus delivers business-critical insights to the global energy industry through a state-of-the-art SaaS platform built on industry-leading data and energy analytics. Our solutions deliver value across the entire energy value chain, empowering customers to be more agile, efficient, and competitive. The range of energy industry participants we serve includes exploration and production (E&P) companies and related businesses such as oilfield services, midstream, capital markets, power generators and utilities, energy traders, and downstream commercial & industrial energy consumers.
We are currently seeking a highly driven Senior Data Scientist to join our Technology team in Conshohocken, PA, Denver, CO, Austin, TX, or Calgary, AB. This role will support the efforts of the Data Science team by producing accurate data science models.
- Build data products to solve business problems in the oil and gas industries.
- Conduct independent research.
- Manage the development of data science product features.
- Mentor junior team members.
- Partner with Data Engineers and Software Developers to evaluate the technical trade-offs of tools to build, simple, yet robust, data science pipelines.
- Write well-documented experiments and production-ready code.
- Advanced degree in Computer Science, Data Science, Statistics, Mathematics, Operations Research, or other quantitative discipline or Bachelor’s degree with 2-4 years related experience.
- Previous experience working on data products.
- Familiarity with Scrum and agile practices.
- Ability to adapt to changing business priorities.
- Solid coding skills in Python, R, or Scala.
- Experience with common Python data science libraries such as Pandas, NumPy, scikit-learn.
- Solid understanding of Linus and git; Spark, PySpark, SparkR or Dask; cloud computing (AWS, Azure, Google Cloud).
- Ability to apply Machine Learning, Optimization, Deep Learning, or Big Data techniques to solve real-world problems.
- Ability to conduct independent quantitative research.
Back to top