Application Developer for Innovation & Engineering

Job Title
Application Developer for Innovation & Engineering

Requisition Number
R2592 Application Developer for Innovation & Engineering (Open)

Walnut Creek, California

Additional Locations

Job Information

Designs, develops and programs methods, models, processes and systems of diverse scope to consolidate and analyze diverse data. Generates actionable insights and solutions to help business leaders and clients make optimal data-driven decisions. Seasoned, experienced professional with full understanding of area of specialization.

Essential Functions / Principal Responsibilities

  • Identifies meaningful insights from large data sources.
  • Performs complex statistical analysis to interpret, quantify and validate trends or patterns.
  • Communicates insights and findings from analysis to business stakeholders.
  • Typically uses well-established statistical models.
  • Contributes to cross-functional analytics projects from beginning to end.
  • Develops relationships with partner teams.
  • Helps frame and structure questions as well as collect and analyze data.
  • May help summarize key insights in support of decision making.
  • Works with developers to strategize on the definition and development of data acquisition processes and to implement analytics solutions.
  • Actively seeks new statistical tools, technology, models and standards for possible use with CSAA core businesses. Periodically presents findings to peers.
  • Verifies model and algorithm effectiveness through ongoing tracking, monitoring and evaluation of results in consultation with more senior Data Scientists, business partners and clients.
  • Develops and maintains consultative relationships with key business stakeholders.

Knowledge, Skills and Abilities

  • Advanced analytics skills
  • Advanced tools knowledge (e.g., SAS, Revolutionary R, other systems)
  • Statistical modeling expertise with ability to create effective predictive models
  • Strong business acumen and knowledge of business processes and CSAA operations and goals
  • Technical aptitude and IT knowledge
  • Solid problem solving skills, including the ability to utilize non-traditional, "outside the box" problem solving skills in a variety of situations
  • Inquisitive orientation
  • Advanced knowledge and understanding of data mining, predictive modeling and data evaluation techniques
  • Detail-oriented with strong organizational skills
  • Proficient project management skills
  • Deep understanding of the mining and analysis of large corpora of structured and semi-structured data; ability to manipulate large amounts of data from multiple sources in order to identify relevant observations, trends and patterns to address business questions
  • Strong knowledge in the following areas: machine learning, statistical modeling, pattern recognition, information retrieval, experimental design, search ranking and entity resolution
  • Knowledge of distributed and real-time stream computing solutions and ability to leverage them towards gaining faster insights from data
  • Knowledge of the software development lifecycle and the ability to interface effectively with engineering teams
  • Solid communication skills

Education, Work Experience, Licenses and Certifications

  • Master's degree in STEM field or Bachelor's degree with an equivalent combination of relevant education and experience
  • Typically 3+ years of quantitative and qualitative research and analytics experience
  • Solid experience with applying data science and tools to large and diverse big data sets
  • Strong background in machine learning and information retrieval with experience managing end-to-end machine learning pipeline from data exploration, feature engineering, model building, performance evaluation and online testing with TB to Peta bytes size data sets. Experience using machine learning techniques: clustering, regression, classification, graphical models and mixture models.
  • Dataset experience in document, graph, log data and semi-structured data
  • Experience with closed loop, self-updating/self-learning model frameworks; distributed version control systems such as git or team SVN; and performing basic tasks with the Linux command line interface
  • Proven track record of delivering high quality analytics insights and solutions

  • Ph.D.
  • Experience in insurance or financial services data along with leveraging third-party relevant structured and unstructured data


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