Data Scientist

C- Quest Capital LLC (CQC) is a social impact investor, project developer, and climate finance specialist with a mission to transform the lives of families in the poorest communities in developing countries by providing them access to clean and sustainable energy services, clean energy technologies and improving the health and wellbeing of women and children. CQC was founded in 2008 and is headquartered in Washington DC, USA with subsidiaries in India, Malaysia, Singapore, and Cambodia as well as on-ground teams in Malawi, Zambia, and rapidly growing into other Sub-Saharan Africa countries.

CQC is a leader in providing clean efficient cooking and lighting services to the rural poor worldwide and has growing businesses in renewable energy production and supply, sustainable agricultural land use, and forestry. CQC’s projects are implemented either through operational platforms and subsidiaries or through closely managed partnerships and joint ventures with on-the-ground organizations.

C-Quest Capital has active investments in over 15 countries across Sub-Saharan Africa, Central America, and South and East Asia that have thus far improved the lives of over 20 million people.

CQC employs over 120 full-time staff of investment, project management, and carbon professionals based around the world including Washington DC, India, Malaysia, Cambodia, Malawi, and Zambia. In addition, CQC regularly draws on a pool of individual consultants, collaborating NGO and private sector partners, and staff from joint venture partners.

Location:

Role and Responsibilities: In this role, you will work closely with the internal IT team to support CQC business units’ data and analytical needs. You will be responsible for responding to internal information requests, implementing and identifying opportunities for automated analytics solutions, and translating analytical outputs for diverse business stakeholders.

In the future, the position may have responsibility to manage a team of junior analysts/scientists embedded across business units while driving enterprise-wide data products. You will have the freedom to evaluate, recommend, and implement new technologies and data products to meet the needs of the company. You don’t have all the answers, but you know where to look and how to efficiently evaluate solutions. You will have support from the IT team and manager, and direct input into timelines and feasibility for your work.

Responsibilities:

• Perform work independently
• Proactively reach out to team for input and guidance when needed
• Manage and complete multiple concurrent projects within given timelines
• Provide input on areas of work that need focus or improvement
• Participate in stakeholder discussions and engagement for data products
• Stay current on methodology advancements and related industry technologies
• Maintain documentation for data products

Requirements:

• 4+ years of experience in a data science or similar role
• Interest in managing or experience mentoring junior analysts / junior data scientists
• Demonstrated experience with geospatial data
• Demonstrated ability to manipulate large data sets in Python, R, and SQL
• Experience visualizing multi-dimensional data using tools like Tableau, ggplot2, Plotly, matplotlib, seaborn, or D3.js
• Ability to apply, modify and formulate algorithms and processes to solve challenging problems
• Git version control for code (e.g., git, GitLab, GitHub)
• Experience working with AWS services (e.g., S3, EC2, EMR, Glue, SageMaker, Athena)
• Ability to apply, modify and formulate algorithms and processes to solve challenging problems
• Ability to work and troubleshoot independently and communicate proactively
• Excellent verbal and written communication skills

Additional Relevant Skills:

• GitLab or GitHub project management tools (e.g., epics, milestones, issues)
• Work in agile development environments, specifically Kanban and Scrum methodologies
• Experience training, productionizing, and continuously improving NLP, CV, or other ML methodologies
• Experience with causal inference methodologies (e.g., propensity score weighting, RCT, RDD, etc.)

Location: Remote