Data Scientist Mid

Cintel IncHuntsville, AL
Onsite

About The Position

We are seeking a Mid-Level Data Scientist to support scientific and technical efforts through the application of data science, statistical methods, modeling, and computational techniques. This role will focus more heavily on scientific problem-solving, experimentation, and developing data-driven solutions than traditional data visualization or business analytics.

Requirements

  • Bachelor’s degree in Data Science, Computer Science, Mathematics, Statistics, Engineering, a physical science, or related field.
  • 6+ years of relevant professional experience.
  • Active Top Secret security clearance.
  • Strong background in data science, statistics, scientific computing, modeling, or a related technical discipline.
  • Experience with Python, R, MATLAB, or similar programming/data science tools.
  • Strong analytical and problem-solving skills.
  • Ability to work independently and as part of a technical team.

Nice To Haves

  • Engineers, Analysts, and Developers at Cintel, Inc. are highly motivated, technical, and self‐organized.
  • We place a lot of trust in our team members to develop technical solutions for ill‐defined problems (i.e. thrive in an environment where the problem is vague, requirements are lacking, and a solution is not obvious).
  • We need problem solvers.
  • We want our team members to be self‐motivated and eager to learn new skills.
  • If you consider yourself a jack‐of‐all‐trades, and are eager to keep up with the latest trends in technology, you’ll fit right in.

Responsibilities

  • Apply data science, statistical, and computational methods to complex scientific and technical problems.
  • Develop and implement analytical models, algorithms, and data-driven methodologies.
  • Analyze and interpret complex datasets to identify patterns, relationships, and insights.
  • Support scientific research, experimentation, modeling, and simulation efforts.
  • Develop and validate predictive or statistical models.
  • Work with cross-functional teams to translate scientific and technical requirements into data-driven solutions.
  • Document methodologies, results, and technical findings.
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