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Data Scientist, Fraud Data Analytics, Enterprise Counter-Fra... - IoT BigData Jobs

Data Scientist, Fraud Data Ana...

Data Scientist, Fraud Data Analytics, Enterprise Counter-Fra…

New York, NY 10002 (Lower East Side area) 2016-11-05 - –


Job Description


Since 1918, it has been TIAA’s mission to serve, our ability to perform and the values we embrace that make us a different kind of financial services organization. We’re dedicated to serving the financial needs of those in the academic, medical, cultural, governmental and research fields, and committed to helping make lifetime financial well-being possible for them.

By building a culture that allows all employees to contribute their unique talents and skills, we’re able to provide our customers with fresh ideas and distinct perspectives to help them achieve their goals. We believe a diverse and inclusive workforce is one of our greatest strengths and a key measure of our success * .

For more information about TIAA, visit our website .


Data Scientist, Fraud Data Analytics, Enterprise Counter-Fraud

New York, NY or Charlotte, NC

The Data Scientist, Fraud Data Analytics within the Enterprise Counter-Fraud organization will report into the Director, Senior Lead Data Manager for Threats & Analytics. This person will be responsible for identification, design, development, delivery and monitoring of analytic opportunities—leveraging existing and new data sources. The Data Scientist will leverage technology and advanced analytical methods to organize, analyze, evaluate and discover new insights. He/she will take ideas from inception to implementation and communicate results to all levels of management.

    • Lead independent efforts for Fraud Data Analytics, applying in-depth knowledge of multiple technologies
    • Leverage technology and advanced analytical models to organize, analyze, evaluate and discover new insights from previously unknown relationships and patterns in both structured and unstructured data
    • Perform advanced analytical techniques, including:

      • segmentation creation
      • mix and time series modeling
      • response modeling
      • lift modeling
      • experimental design
      • neural networks
      • data mining
      • optimization techniques
    • Data visualization and presentation
    • Apply scientific techniques to validate outcomes
    • Present data from tested outcomes in order to solve complex business problems and identify new business opportunities – including written and verbal presentations
    • Provide leadership, guidance and develop technical capabilities across the Fraud Data Analytics team
    • Attend meetings as delegated for the Director, as needed



  • 5-7+ years of Fraud Data Analytics experience required.


  • Possesses excellent numeracy and understanding of advanced analytical techniques including:

    • segmentation creation
    • mix and time series modeling
    • response modeling
    • lift modeling
    • experimental design
    • neural networks
    • data mining
    • optimization techniques
  • Solid understanding of data structures and databases in structured & unstructured environments
  • Versed in statistical analysis packages, such as SAS, R, RAT, SPSS, etc.
  • Demonstrated experience in data management tools / relational databases (such as Oracle, Teradata, SQL Server), Data Manipulation tools (such as DataStage, Informatica)
  • M.S. in a relevant field, such as Applied Math, Statistics, Computer Science, Physics, Economics, Electrical Engineering, or Bioinformatics
  • Ph. D.
  • 10+ years of relevant work experience
  • 3+ years in a financial institution
  • Experience with financial asset fraud
  • Experience performing natural language parsing and entity extraction from pdf
  • Working knowledge of newer data technologies such as Hadoop, MapReduce, PIG, HIVE, Python, noSQL, MongoDB, Oracle Exalytics
  • Analyzing business requirements as a guide to data modeling
  • Leveraging a suitable data modeling approach for each project and assessing the suitability of existing data models
  • Building data models with the flexibility to change when business requirements change
  • Reconciling multiple logical source models into a single, logically consistent model
  • Ensuring the data model follows guidelines, standards, and best practices
  • Rationalizing the relationships expressed in the logical data model to the schema of the physical data store
  • Working with the IT teams to implement data strategies, build data flows and develop conceptual data models
  • Create logical and physical data models using best practices to ensure high data quality and reduced redundancy
  • Maintain conceptual, logical and physical data models along with corresponding metadata
  • Perform reverse engineering of physical data models from databases and SQL scripts
  • Evaluate data models and physical databases for variances and discrepancies
  • Validate business data objects for accuracy and completeness
  • Analyze data-related system integration challenges and propose appropriate solutions
  • Examine new application data design and recommend corrections
  • Additional registration may be required (FINRA Series 99 Operations Professional) within the first 90 days of employment.

Equal Employment Opportunity is not just the law, it’s our commitment. Read more about the Equal Employment Opportunity Law .

If you need assistance applying due to being visually or hearing impaired, please email Careers Help .

We are an Equal Opportunity/Affirmative Action Employer. We will consider all qualified applicants for employment regardless of age, race, color, national origin, sex, religion, veteran status, disability, sexual orientation, gender identity, or any other legally protected status.

* ©2016 Teachers Insurance and Annuity Association of America (TIAA), 730 Third Avenue, New York, NY 10017 C23921

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