We are seeking an experienced professional with a strong background in statistics, analytics, and marketing to join the MarketBridge Business Analytics & Technology practice. As a Data Scientist, you will be the responsible for designing and delivering statistically based sales or marketing solutions that align technical methodologies to business needs. Successful candidates will learn new concepts quickly, stay abreast of advances in marketing technology and related fields, and apply their wide-ranging experience to creatively and quantitatively solve our clients’ business problems.
The job responsibilities include:
Predictive Modeling: Build predictive models to optimize sales and marketing programs. Candidate must be comfortable using logistic regression, linear regression, discriminant analysis, neural networks, CHAID, CART and other statistical techniques.
Segmentation: Develop customer segmentation solutions using K-means clustering, factor analysis, principle components and other statistical techniques.
Data Manipulation & Analysis: Work with internal and external project teams to understand, manipulate and analyze client and market data using various tools, which may include: MS Excel, SQL Server, BI tools such as SSRS or Tableau, and statistical programs such as R
Client Relations: Contribute to positive client relationships while collaborating with internal client team members. Partner with teams to define analyses, develop hypotheses, gather data, brainstorm alternatives, and generate recommendations.
We typically work in teams of two to six people. Successfully working in a team, while maintaining a strong personal work ethic, is essential for success at MarketBridge.
For this position, we are looking for experienced hires with the following characteristics:
Master’s degree or higher in a quantitative field (statistics, applied math, econometrics) with a strong focus on statistics
- Strong proficiency in R
- 1-3 years of experience using multivariate statistical techniques to solve sales & marketing problems. Examples include predictive analytics, explanatory analytics and measurement, prospect modeling, retention modeling, customer lifetime value, customer segmentation, content optimization, regression, survival analysis, etc.
- Ability to explain the value of and outputs of statistical analysis to a business user in a clear and articulate way
Comfort with ambiguity and incomplete requirements or direction
- Ability to demonstrate knowledge and application of industry best practices for modeling
- Detail-oriented with an ability to support multiple projects at once
- Strong mastery of Microsoft Office tools (specifically PowerPoint and Excel)
- Excellent written and oral communication skills
- MS SQL Server or other database experience is highly valued
How to apply:
If you are interested in this opportunity, please send us your CV and motivation letter in English, at firstname.lastname@example.org. with subject line: Marketing Analytics Data Scientist (tuk-tam).