Lead Data Scientist

Lead Data Scientist

FarePilot(www.FarePilot.com) is a service provider created for Private Hire Drivers based on
geographical recommendations, and powered by machine learning.
Firstly, we assist taxi drivers to find customers faster based on their location, linking to Google Maps and Waze to get them there in the most intelligent and efficient manner. Secondly, we have a function called ‘Offers’, which provide specific offers for taxi drivers, i.e. discounted fuel, and thirdly we have an additional function called Money Management, which helps drivers manage their jobs, income and expenses at the click of button.
The current team size is just over 20 people including both technical and commercial experts (designers, developers, engineers, product managers, data scientists, marketers and business operations).
Role Summary
We are seeking strong candidates with advanced analytics experience to lead our Data Science team and start an exciting Data Scientist career within the start-up FarePilot.
The role provides an opportunity to design and build analytics methodologies, solutions, and products to deliver extraordinary value to FarePilot users in collaboration with software engineers, designers and product managers.
Exceptional candidates will show an analytical curiosity going beyond the immediate requirements of the venture to find deep insights that others have missed. They will ask questions about outliers, seek to understand the fundamental drivers of advantage and
look for clues that may change the basis of competition.
As the field of advanced analytics is rapidly evolving, all members of the FarePilot team are responsible for staying current on leading-edge business applications, tools and approaches, proactively working with the leadership to enhance offerings that deliver competitive advantage to FarePilot.
The Lead Data Scientist (LDS) will be involved in all aspects of advanced analytics, from helping to create relevant analysis and service offerings by leading and executing analytics work and continuing to expand the analytical foundation and competitive value proposition.
The LDS will collaborate directly with the management and wider venture team and will manage the analytics components. The LDS is responsible for clarifying initial objectives,
setting up analytics work plan and methodology, organizing the data scientist members of the team, quality assurance, and managing scope and work planning throughout the project.
The LDS is expected to provide mentoring, coaching, and career development to Junior Data Scientists on both a formal and informal basis.


  • Degree in a field linked to computer science, applied mathematics, statistics, machine learning, or related data centric areas, or
  • Relevant work experience of 5+ years
  • Passion for and interest in data science topics
  • Autonomous self-starter, drive and energy, and desire to work in a Start-up environment
  • Creative, yet structured problem solver
  • Able to work in a fast-paced environment and to manage multiple tasks in parallel
  • Strong interpersonal credibility, reliability, and service mentality
  • Highest ethical standards, able to maintain discretion and confidentiality
  • Written and verbal communication skills in English, preferably also in a second language

  • Technical competencies
  • Experience in the following analytics methods (two or more of the following):
  • Machine learning: e.g. Random Forest, neural networks
  • Predictive modelling: e.g. logistic regression, linear regression
  • Geographic Analysis (location-allocation, travelling salesperson, vehicle routing problem)

  • Strong candidates additionally have experience with one or more of the following methods:
  • Geographic cluster recognition and manipulation techniques
  • Statistics (t-tests, ANOVA)
  • Variable reduction (FA, PCA)
  • Segmentation/clustering techniques
  • Time series analysis: e.g. ARIMA, VAR, etc.
  • Text mining & unstructured data analytics
  • Agent-based simulation
  • Optimization, e.g. linear programming, heuristic approaches

  • Familiarity with a broad base of analytics tools – preferably one or more per category:
  • Data management, e.g. Alteryx, Excel, SQL, PostGRESql, or similar
  • Analytics platforms and languages, e.g. R, Python, RapidMiner, SPSS, or similar
  • Data visualization, e.g. Tableau, Qlik, or similar o Geographic information system (GIS): ESRI incl. Network Analyst, Quantum GIS, MapInfo, or similar

  • Preferably experience in applied analytics for business problem solving/experience building analytical solutions (as a plus)
  • Delivery fleet optimization
  • Loyalty program effectiveness
  • Customer segmentation and targeting
  • Churn prevention

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