Job Description
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.