Lead / Principal Data Scientist (m/f)

Lead / Principal Data Scientist

• 7+ years of relevant industry work experience providing advanced analytics solutions, or 5+ years consulting experience
• Advanced degree required in a field linked to business analytics, statistics or geo-statistics, operations research, geography, applied mathematics, computer science, engineering, or related field
• Deep technical and data science expertise, acute strategic and analytical skills, ability to lead and persuade, drive and energy, and desire to work in a project based environment on strategic issues.
• Strong record of professional accomplishment and leadership
• Demonstrated ability to lead and manage projects and teams

Experience in core analytics methods (one or more of the following):

• Statistics (t-tests, ANOVA)
• Variable reduction (FA, PCA)
• Segmentation/clustering techniques
• Geographic cluster recognition and manipulation techniques
• Predictive modeling: e.g. logistic regression, linear regression
• Network analysis (location-allocation, travelling sales person, vehicle routing problem)
• Time series analysis: e.g. ARIMA, VAR, etc.
• Machine learning: e.g. LCA, Random Forest, neural networks
• Spatio-temporal analysis
• Time series analysis (ARIMA, VAR, etc.)
• Text mining & unstructured data analytics
• Simulation, e.g. MC, dynamic, discrete event
• Optimization, e.g. linear programming, heuristic approaches

Familiarity with a broad base of analytics tools

• Data management, e.g. Excel, SQL, PostGRESql, Hadoop/Hive, Alteryx
• Analytics platforms, e.g. R (preferred), SAS, RapidMiner, SPSS
• Data visualization, e.g. Tableau, GIS toolkits (ESRI, Quantum GIS, MapInfo or similar), ESRI Network Analyst, RouteSmart, RoadNet or similar, GPS data analysis a plus
• Programming and/or scripting exp., e.g. Python (preferred), C#, VBA, Java, Perl, etc.

Experience in applied analytics for business problem solving: experience building analytical solutions. Preferably one or more of the following, others a plus:

• Pricing and promotional effectiveness
• Delivery fleet consolidation
• Loyalty program effectiveness
• Network real estate reorganization
• Customer segmentation and targeting
• Delivery footprint/territory expansion (or reduction)
• Customer LTV maximization
• Cost modeling of transportation & logistics operations
• Churn prevention

For consideration please send us your CV as well as your academic certificates and salary expectations to tomf@venquis.com and we will be in touch for an initial discussion.