M-KOPA Solar Data Scientist Job in Nairobi, Kenya

M-KOPA is seeking:
Position Title: Data Scientist
Location: Nairobi, HQ Office
Position Start: As Soon As Possible
Reporting to: Head of Data
Overall Purpose: An
experienced Data Scientist with strong business acumen, a talent for
exploring data, and an ability to blend qualitative and analytical
skills to solve business problems.
The Data Scientist
will work with multiple teams in the development and implementation of
predictive, diagnostic and prescriptive analytics


About M-KOPA Solar: M-KOPA Solar, headquartered in Nairobi, Kenya, is the global leader of “pay-as-you-go” energy for  off-grid customers.  

Since its commercial launch in October 2012, M-KOPA has connected more than 250,000 homes in Kenya, Tanzania and Uganda to solar power, and is now adding over 500 new homes each day. 

The success of M-KOPA (M= mobile, KOPA= to borrow) stems from making solar products affordable to low-income households on a pay-per-use installment plan. 
Customers acquire solar systems for a small deposit and then purchase daily usage “credits” for US $0.45, or less than the price of traditional kerosene lighting.  After one year of payments customers own their solar systems outright and can upgrade to more power. 

All revenues are collected in real-time via mobile money systems (such as M-PESA in Kenya) and embedded GSM sensors in each solar system allow M-KOPA to monitor real time performance and regulate usage based upon payments.  
This connected design means that M-KOPA is processing vast amounts of data (i.e. over 10,000 mobile payments per day) via the company’s proprietary cloud platform, M-KOPAnet.

As of May 2015 M-KOPA employs over 650 full time staff across East Africa and sells through a network of 1,000 direct sales agents. It has also commenced licensing its technology to partners in other markets.  

M-KOPA has been recognised for its pioneering business mode and scale, notably winning the 2015 Zayed Future Energy Prize, being selected as the top New Energy Pioneer at the 2014 Bloomberg New Energy Finance awards and earning the 2013 FT/IFC Excellence in Sustainable Finance Award. 

Role Profile
Key Accountabilities
  • The Data Scientist will lead discussions with teams cross functionally on analytics-driven outcomes, as well as leading in the development, and operationalization of statistical projects, which will involve large, disparate, complex data sets. 
  • The aforementioned teams will include engineers, data analysts, software developers, and business users from all departments.
  • Building mathematical models and data models, manipulate data, create diagnostic, predictive, and prescriptive statistics to describe a situation and make recommendations for action.
  • The Data Scientist will act as a thought-leader within the organization, sharing and communicating analytical best practices to others within and outside of the immediate team
  • Work with a variety of data sources from flat files, RDBMS, and “big data” sources to identify the necessary data elements, determine analysis opportunities, and apply data mining techniques to explore the data.
  • Leverage proven written and oral communication skills to convey complex issues to executive level management in a simple and straight forward manner.
  • Work with the Business Intelligence team to continuously enhance our data warehouse to address a variety of use cases across the enterprise.
  • Work in an Agile environment, delivering interim solutions quickly and continuously exploring ways to improve our results
  • Actively seek out new potential problems to solve, assist in building out our future roadmap while simultaneously executing our present analyses
  • Creating Excel and PDF-based reports
Job Requirements
  • Required: 5+ years of hands-on experience performing analytics, data mining and modeling
  • Required: Advanced SQL skills (data modelling, TSQL or equivalent)
  • Required: Advanced SQL skills. Experience with writing complex SQL queries as well as extensive experience working with relational and dimensional data models
  • Required: Advanced Excel skill; writing macros and demonstrable knowledge of Excel’s BI capabilities.
  • Required: Familiarity with a data analysis programming language (e.g. Python, R)
  • Comfortable working in cross functional groups, supporting multiple business clients, and presenting to Senior Executives of varying levels of technical understanding.
  • Strong affinity for Mathematics, Statistics and Data visualization
  • Experience working with Visualization software such as Tableau, Power Bi, QlikView, etc
  • Experience deploying machine-learning techniques on an enterprise-scale
  • Ability to blend qualitative and analytical skills, problem solving and strategy consulting with a strong bias to action.
Nice to have:
  • Experience in open source programming languages such as R for large scale data analysis
  • Experience with Hadoop environment, Pig, Hive, Map Reduce programing, other Hadoop services.
  • Experience with scripting language such as JavaScript, Unix Shell programming
Personal traits
  • Strong business acumen and ability to translate data patterns to business processes.
  • Be a collaborative teammate, sharing knowledge and learning from others while working through complex problems
  • Always on the look-out for the next challenge. The ideal candidate will be curious to explore, and always asking “why”.
Remuneration: Competitive, covering a monthly salary, performance bonus and medical benefits reflective of the candidate’s experience and skills.
How to Apply
To apply, send an updated detailed copy of your CV and a cover letter expressing why you feel you would be an excellent candidate for the role to careers@m-kopa.com with the subject EV-DS-2062

Deadline for applications is noon on Wednesday 10th February, 2016.

Please Note: Due to the large number of applications received by us, we regret that you will not be contacted unless you are short listed for the post and invited for an interview. Therefore, if you have not heard from  M-KOPA within 4 weeks of the date of the deadline your application, you should assume that you have not been successful on this occasion.

This measure has been taken in the interests of efficiency and cost effectiveness and we apologize for any inconvenience this may cause.

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