Job description
This is an incredible opportunity for a mission-driven data guru who seeks a purposeful, long-term career benefiting the lives of over a billion underserved consumers in Sub-Saharan Africa and Asia: Come lead the data science team for one of the world’s most innovative, life-changing, growth-stage companies, expanding solar energy access and financial inclusion in exciting emerging markets!
Greenlight currently employs data scientists, data analysts, and app developers within multiple internal teams, as well as external service providers. The company seeks to hire a senior technologist with excellent quantitative and technical skills to lead and mentor a centralized, world-class data science department. The key outputs of the department will include risk analysis and loan underwriting for lean-file consumers, sales-force and sales-process optimization, product design improvement through analysis of IoT-derived usage and behavioral data, and overall business intelligence.
The VP of Data Science will be a hands-on technology leader, growing and mentoring a team, while retaining a lean, entrepreneurial approach. She or he will work with culturally diverse internal and external teams across the world, and should be comfortable working in a less rigid, more entrepreneurial, more geographically diverse, growth-stage company.
Key responsibilities
1. Data Strategy & Operations
-Determines the data strategy and operating model for the growing organization, which will include dramatic expansion of the company’s efforts in:
Data aggregation strategy (via, for example, Greenlight’s GSM cloud-connected energy systems, mobile apps handled by Greenlight field agents, Greenlight customer support call centers, SMS surveys, data partnerships with other companies/organizations touching the same consumers)
Data engineering, including architecture and technology
Data science and analytics, including traditional models and machine learning / artificial-intelligence approaches, for multiple purposes across the company. Priority areas ripe for innovation include:
Credit risk prediction and analysis
Loan underwriting for lean-file consumers
Customer portfolio segmentation to inform pricing and opportunities for cross-sale
Sales force and sales process optimization
Product design improvement through analysis of IoT-derived usage and behavioral data
General business intelligence
-Utilizes data insights to drive innovation – new products, systems, business initiatives
-Vets and selects third-party software providers for core banking, CRM, and ERP systems, that feed data to the business (or, if third-party software is not well suited to the purpose, develops in-house solutions to needs currently addressed by third-party software).
2. Leadership and management
Assess talents and capabilities of existing data scientists, programmers, and analysts, how to leverage these, and how to build out the team with new hires over time.
Build the business’s data talent needs through role definition, recruitment, and development of a team who will jointly move the business’s agenda forward.
Provide leadership and guidance to the data team.
Skills and experience
First and foremost, strong quantitative, statistical, and computer science aptitude
Expertise in SQL (AWS Redshift or Postgres preferred)
Expertise in statistical analysis and predictive modelling in modern languages (Python, R, or similar)
Orientation towards cloud-based data engineering (AWS preferred, either direct experience or experience overseeing specialists in this area)
Orientation towards machine learning and artificial intelligence approaches (either direct expertise or experience overseeing specialists in this area)
Proficiency with modern off-the-shelf BI / data analytical tools (Preferred: Looker)
Proven track record building innovative and successful data products, including demonstrated
Ability to perform advanced feature engineering
Ability to utilize both linear and non-linear models, as optimal for a given problem / product maturity / datasets
Ability to utilize both traditional and non-traditional data sources for predictive analytics
At least five years in senior data roles, including people management experience
Orientation towards credit risk analytics and loan underwriting / credit check processes, preferably in a fintech or consumer lending environment (ideally not a big traditional bank, unless in an innovation hub or similar)
Experience managing, mentoring, and retaining a nimble, lean data team
Personal attributes
Excellent communication skills
Good commercial acumen, to derive commercially meaningful insights
Strategic and conceptual thinker with ability to translate strategy into plans and deliverables
Preference to collaborate across departments and cultures
Resilient, agile and flexible
Cost-conscious, with the ability to work in an entrepreneurial environment