Data Analytics Officer

Main Role 
The Data Analytics Officer will support the LRC with analyzing the integrity of the data pipeline, mining patterns in the data, and modeling potential operational improvements to  enhance LRC services. This will require the design and use of optimization techniques to model and test the effectiveness of different courses of action. The Data Analytics Officer must have strong experience with descriptive, predictive and prescriptive analytics techniques including but not limited to the use of a variety of data mining methods the capability to work fluently with a variety of data tools and scripting languages (R, Python, etc), and the ability to build test, and implement decision support algorithms through simulation. 

Essential Roles and Responsibilities 
• Maintain the integrity of the data pipeline – regularly verifying data collection procedures and the correct flow of data from source to analysis, ensuring timely backup, and secure access.

• Improve the data pipeline by identifying valuable data sources and automating the collection processes

• Identify and extend LRC data with third party sources of information when needed

• Process, cleanse, and verify the integrity of both structured and unstructured data

• Use state-of-the-art techniques to analyze large amounts of live data with the aim of uncovering actionable trends and patterns

• Create automated anomaly detection systems for constant performance tracking Propose tested solutions and strategies to business challenges

• Use state-of-the-art data visualization techniques to present information for consumption by diverse audiences  

Experience Required

• Proven experience as a Data Scientist or Data Analyst
• Experience in data mining
• Knowledge of R, SQL and Python; familiarity with Scala, Java or C++ is an asset
• Experience using business intelligence tools (e.g. Power BI and Tableau) and data frameworks
• Analytical mind and business acumen
• Strong math skills (e.g. statistics, algebra)
• Experience creating and using state-of-the-art analytics tools including: regression, machine learning algorithms (clustering, decision trees, neural networks, etc), scenario analysis, simulation, and mathematical modeling.
• Experience analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Core metrics, AdWords, Crimson Hexagon, Facebook Insights, etc.
• Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
• Demonstrated ability to work collaboratively with other data scientists as part of a team
• Previous experience in working with NGOs is a plus