Data Scientist - R01555477
Job Description
- Strong understanding of electrical grid engineering concepts.
- Proficiency with power system analysis tools like OpenDDS, Matpower, PSS/E.
- Experience with DER impacts on grid operations and planning.
- Ability to apply advanced analytics on big, unstructured data sets.
- Experience in designing databases for unstructured data.
- Skilled in exploratory data analysis and building complex data sets.
- Collaboration with Data Engineers on data pipelines.
- Defining and directing data science strategies.
- Designing complex data science models for predictions.
- Knowledge of latest research in data science and ML.
- Expertise in Python for automated analytics processes.
- Experience in designing testing strategies for ML algorithms.
- Leading proof-of-concept prototypes.
- Stakeholder management for model implementation and monitoring.
- Leading agile data science teams.
Primary Skills
- Electrical grid engineer
- data sets
Job requirements
- Electrical grid engineer
a. Understanding of
i. System protection
ii. Distribution automation
iii. 3 phase power system analysis
iv. distribution design, planning, or control/operations
v. SCADA and AMI data
vi. CIM network model
vii. Load flows
- OpenDDS, Matpower, PSS/E, Cyme, pypower, PSAT, GridCal, or GridLAB
b. DER impacts on grid operations, control, and planning
c. Apply advanced analytics techniques through broad parameters on big, unstructured data sets; works with most complex big data sets
d. Provides guidance on design of databases for unstructured data sets, defining how the data will be stored, consumed, integrated and managed
e. Directs exploratory data analysis from complex data sources and builds complex data sets
f. Collaborates with Data Engineers to identify relevant data sets; engages with Data Engineers to review and provide feedback on data pipelines, and optimization opportunities
g. Takes an active role in determining overall data science strategies, and helps define and direct the continuing evolution of the data science discipline to meet the changing business needs of the enterprise
h. Monitors the effectiveness and performance of data science solutions and services to the business and directs efforts to continually improve the business value of these services
i. Designs complex data science models to predict outcomes or impact of key decisions; contributes to development of critical, transformative business strategy
j. Reviews and applies latest cutting-edge research on data science and model development
k. Brings external machine learning point of view and best practices to the organization
l. Oversees the design of automated, operational analytics processes to achieve scale and durability of analysis processes using Python
m. Designs testing strategy for evaluating algorithms and machine learning system n. Leads development of proof-of-concept prototypes to prove out hypotheses
o. Manages stakeholders across multiple functions to implement the models into production and monitors their performance
p. Acts as data science lead on agile teams; leads and guides team members on development practices in agile environments; collaborates with scrum masters and coaches to move projects forward
