The Lead Data Analyst will evaluate the return of investment
of Tesla Energy training, visualize training data to relevant audiences, and
shepherd data from source to destination. You will work closely with the
Training Programs team to determine the ROI, relevant metrics, and break-even
point for proposed training programs. You will create and present metrics for
business stakeholders. As the leader of Evaluation, you will own all data
analytics, model building, and data communication tasks required by the energy
training team, and our partners.
Responsibilities
- Summarize and clearly communicate data analysis
assumptions and results to inform strategic workstreams - Maintain and update the portfolio of excel
documents used to manually track results not yet automated - Report via email biweekly to business leader on
the value and impact of training - Communicate with internal training stakeholders
to deliver feedback loops - Design and implement metrics, applications and
tools that will empower energy by allowing them to self-serve their data
insights - Maintain robust documentation and support your reports
- Keep up to date on relevant technologies and
frameworks, and propose recommendations that the team can leverage to optimize results
Required Qualifications
- Bachelor’s Degree in related analytical field
- Expert level proficiency in Microsoft Excel
- Experience building data visualizations hosted
on existing web-based platforms e.g. (Kibana, Tableau, Superset, Power BI) - Experience with an agile workflow (Jira, GitHub,
etc.) - Experience with python and data analysis
libraries, Pandas, NumPy, MatPlotLib, etc. - Experience with ubuntu or operating a Linux
virtual machine via command line - Smart but humble, with a bias for action
- Learning Management System experience
- Exceptional interpersonal and communication
skills - Knowledge of the Kirkpatrick Model
Preferred Qualifications
- Experience in Learning and Development ADDIE
style org - Statistical skills such as utilized in A/B
testing, analyzing observational data, and modeling - Experience building data pipelines
- Experience with continuous integration and
continuous development - Proven track record of leveraging massive
amounts of data to drive innovation - Experience with distributed analytic processing
technologies (Spark, Presto, Hive)
