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Nairobi, Kenya

Salary rangeConfidential | Contract type: Permanent

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Job Summary

M-KOPA is looking for a Data Scientist who will be responsible for analysis and transformation of engineering data from distributed M-KOPA IoT devices into product/behavior insights and, finally, recommendations to multiple departments working on e.g. hardware, device firmware, product design, supply chain management, engineering testing etc.
The Data Scientist will use device characteristics (product use, battery health etc.) to provide technical decision support for warranty cases, personalized customer offerings, and loan repayment prediction.
The Data Scientist will create new analyses from existing data and assist in developing long term strategy for future data collection capability and data services/analysis. Results and conclusions will be communicated with relevant stakeholders and most valuable analyses integrated with existing business systems for continuous automatic reporting.
The Data Scientist will translate knowledge requests from internal and external stakeholders into technical requirements and give recommendations on suitable approaches based on likelihood of outcomes, general conditions, and time estimates.


The Data Scientist will
· Develop models of device performance and customer use patterns to be used as tools for engineering product design and commercial strategy
· Investigate warranty cases and other device anomalies
· Use device status metrics to personalize customer offerings and predict repayment performance
· Evaluate component performance and verify suppliers’ adherence to expected quality
· Recommend new types of engineering analyses based on available data
· Recommend and/or develop data collection methods (hardware, firmware) and data services
· Identify product design vulnerabilities and over-capacity
· Support Supply chain/Operations strategic decisions with device performance data
· Develop strategy for device monitoring pilots and future product data capabilities
· Quantify device utilization and performance so that analysts and Business Partners can use device data as input to other models (e.g. financial models)
· Provide support and mentoring to other analysts within domain areas energy, electrical engineering, and battery theory
· BSc in quantitative field (Statistics,Computer Science, Mathematics, Engineering, Physics etc.) and/or equivalent experience with independent learning resulting in suitable skill set for effective performance in the role
· Masters or PhD level training in a statistically, mathematically, and/or computationally intensive field would be considered an asset (preferred)
· Technical training in Data Science, Data Analytics, and Data Management/Engineering (preferred)
· Technical training in Energy, Electrical Systems/Circuits, On/Off-grid systems and Battery Theory would be considered an asset (preferred)
· Professional or post-graduate experience in a data science or data engineering role would be a significant asset. However, for a candidate with the appropriate technical skills and a passion for the role, this could be structured as an entry-level position
· Product development experience (hardware or software) would be considered an asset (preferred)
· Teaching/mentoring and/or leading a technical team would be considered an asset (preferred)
Knowledge / Skills:

  • Advanced skills in a data science programming language (Python or R)
  • Experience with SQL and Excel
  • Ability to think creatively about business and engineering problems and understand how to apply data science processes to create measurable results
  • Ability to communicate technical details visually and in written form to broaderstakeholders
  • Meticulousness in ensuring error-free, high-quality analyses
Additional assets
  • Experience with engineering analysis and modeling for energy, electricity, batteries,and/or power electronics
  • Experience with Microsoft Azure (U-SQL, Data Factory, Data Lake), other Big Data tools (Hadoop, Spark), or other Data Engineering tools for system integration of dataanalysis into business applications
  • Experience with modern machine learning methods for signal processing / time-series analysis (e.g. HMMs, Kalman Filters, LSTM Neural Nets, etc.)
  • Familiarity with agile development processes, unit testing, source control,continuous integration, etc.

Job Requirements

Required education: Post-graduate education
Required relevant work experience: 3 years
Required languages: English (Spoken: fluent | Written: fluent)
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