Senior Data Scientist / Predictive Modeler

  • IDentrix/Endera
  • Bethesda, MD, United States
  • May 11, 2017
Full-time Cybersecurity Data Science Java MATLAB NoSQL Predictive Analytics R SAS SPSS SQL

Job Description

IDentrix/Endera provides a first-of-a-kind continuous insider-risk monitoring, assessment and management platform, helping enterprises detect, anticipate and prevent or reduce workplace risk of fraud, theft, violence or catastrophic accidents in a variety of industries.

The Senior Data Scientist / Predictive Modeler position offers a unique opportunity with a financially-sound spin-off company, which provides competitive compensation, fast growth opportunities, and an entrepreneurial drive to innovate.

The successful candidate will be passionate and creative about building algorithms and models based on multiple, very-large data sources, solving complex analytics and data problems, and supporting the implementation of these to continuously improve products and results for our clients.

The position requires substantial experience in the entire data life cycle of large and varied datasets, from collection to exploratory analysis, treatment, interpretation, statistical analysis and reporting, and effective presentation, including visualization of results. It also requires expertise in advance data mining techniques, statistical predictive modeling, machine learning, and natural language processing.

Key Responsibilities

  • Develop requirements for acquisition of new data.
  • Establish metrics for, and evaluate, quality and coverage of data from different sources/ providers; explore and identify potential new sources for higher quality data.
  • Review and analyze data, to evaluate and improve data quality, content, completeness and relevance; use appropriate ETL tools to treat, transform and connect data as needed.
  • Analyze data related to the same individuals and events over time and identify correlations, clusters and time-series properties of related events.
  • Prepare pooled data and construct attributes to be used as key leading indicators of risk and for predictive models, such as risk scorecards.
  • Develop predictive models, both rule-based (judgmental) and statistical, to predict behavioral risks of clients’ employees; develop segmentation for these models and test/ validate their performance.
  • Work together with IT engineers to implement predictive models and ad-hoc solutions and to improve client-facing product features.
  • Evaluate and select best algorithms and software tools needed to carry out said responsibilities; customize algorithms to improve process efficiency and results.

Qualifications and Experience

  • Education: Master's degree (PhD preferred) in a highly-relevant analytical or technical field, such as data analytics, data science, computer science, information systems, operations research, statistics.
  • Minimum 7 years (5 with PhD) of work experience in a highly analytical, quantitative, data-driven environment, with proven success in solving complex analytical and technical problems and showing progression of responsibilities over time.
  • Comfort with manipulating and analyzing very large data from sources of varying data types, formats, quality and completeness; data mining expertise.
  • Significant experience with statistical data analysis and strong skills in a supportive software (e.g., R, SAS, SPSS, Matlab).
  • Working knowledge of the following:
    • Text mining and analytics, natural language processing
    • Relational (SQL) and NoSQL (e.g., Mongo) databases
    • Visualization and dashboarding tools
  • Experience in predictive modeling and scorecard development based on very large data, using statistical (e.g., regression, CART, clustering) and/or machine learning (e.g., ANN) techniques, both supervised and unsupervised.
  • Fluency in a modern programming language (e.g., Java, C++, Python, Perl)
  • Desired: cybersecurity training or experience.

Important Personal Traits

  • Excellent verbal and written communication skills
  • Natural curiosity and interest in learning
  • Creativity
  • Being results-driven, self-starter and a team player
  • Ability to thrive in a high-paced environment and prioritize work based on objectives and changing milestones and deadlines.
  • Potential to mentor and lead junior analysts.

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