Data Science Resume – Top Candidate

Click below for .PDF

Sept 2014 data science resume

PROFILE

Senior Consumer Behaviour Analyst with extensive experience designing customer behaviour predictive solutions to inform strategic business decisions.  Managed a data science team and communicates directly with clients.  Expert in innovating analytic product methodologies and creating process efficiencies in:  Data quality control, analytics and reporting. Additional demonstrated strengths:

 

  • SAS, SQL, SPSS, R and Python
  • Hiring, training and mentoring data scientists and coordinating the team
  • Structural Equation Modeling (SEM) using Mplus
  • Strategic segmentation
  • Customer transactional-journey analysis

 

EDUCATION

1989                             University of Wisconsin, Madison – M.A., Major:  Mathematics

1984                             University of Toronto – B.Sc., Major:  Specialist Program in Mathematics (graduated with High Distinction)

Affiliations:

2005 to Present              Market Research and Intelligence Association

2008 to Present              Institute for Operations Research and Management Sciences

 

TECHNOLOGIES

SAS | SQL | R | SPSS | Python | Mplus | LatentGOLD

Sawtooth Software for Conjoint, Max Diff and Clustering techniques

Java | Microsoft Office

 

PROFESSIONAL EXPERIENCE

 

2004                                                       Pharaceuticals

to 2014                                                   Toronto, Ontario

SENIOR ANALYST

Wrote algorithms designed to better predict needs of customers for the end use of pharmaceutical companies.  Designed and constructed environment for customer behaviour predictive analytics department.  Led the data science team (2 direct reports).

 

  • Invented a customer buying-process research product for optimizing message and advertising development, forecasting demand and lift modelling. Led the development team designing the product sales pitch and results presentation schema
  • Designed and implemented cutting edge segmentation products, embedding client business objectives in the input selection, solution generation and solution selection processes. Approximately 60 segmentation studies and $9 million of revenue in 10 sectors of in-sync clients.
  • Originated the small-sample ‘micro-segmentation’ practice for hard to recruit populations. Lead its evolution and implementation using Artificial Intelligence algorithms and neural networks to incorporate client strategy and strategic
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