Lead Data Scientist – Smart EV Charging algorithms
Paris, France or Copenhagen, Denmark
Data Science | Data Analytics | Advanced Mathematics | Statistics | Programming languages | Hacking | C & C# | R, Python, SQL | Optimization | Predictive Models | Forecasting Algorithms | Data Mining | Machine Learning | Electric Vehicles | Smart Grid | EV Charging | Distributed Energy
Intelligent Employment are recruiting for x2 Lead Data Scientists for one of the most exciting and innovative start-ups within Electrical Vehicles, EV Charging and Smart Charging algorithms, worldwide. We are seeking one Lead Data Scientist to specialise in Optimization, and the other to specialise in Predictive Models. These positions can be situated either in Paris or in Copenhagen.
- Design, test and improve optimization algorithms and/or forecasting algorithms for company software platform and analytics needs
- Contribute to the next generation of products, in close collaboration with product management, engineering and QA teams to including requirements, architecture, algorithmics and QA
- Design and apply new data analytics methods on EV and electricity market datasets to support and anticipate business development needs
- Solve problems using algorithms and data with an emphasis on product development.
- Function as a hands-on, autonomous self-starter: capable of framing a problem, proposing an approach to designing a solution, designing and developing those algorithms, analyzing the data, explaining results in writing or verbally to specialists and non-specialists, iterating.
We would like to hear from you if you possess the following experience & competencies;
Expert-level track-record in the field of optimization algorithms applied to smart charging, storage or smart grids, as evidenced by a track-record of scientific publications, software and/or patents
Programming / Software skills:
- At least 5+ years of experience with Python and SQL,
- Working knowledge of C and C#
- Experience using statistical computer languages (R, Python, SQL, etc.) and associated libraries to manipulate data and draw insights from large data sets.
Solid mathematics and statistics background:
- Math: strong undergraduate level foundations in linear algebra, calculus, and probability
- Statistics: good understanding of basic statistics and knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
Knowledge and Experience with data science tools and techniques:
- Statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
- Machine learning techniques: clustering, decision tree learning, artificial neural networks, etc. and their real-world advantages/drawbacks.
- Querying, cleaning, merging, preparing datasets
- Distributed data/computing tools (e.g. Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL...)
- Visualizing/presenting data for stakeholders using presentation/ visualization tools (Jupyter, Mathematica, Periscope, Business Objects, D3, ggplot...)
General knowledge and familiarity with the vehicle-grid integration domain, including:
- the electricity industry value chain and business models
- EV charging infrastructure from the technology and business standpoint
- Energy markets
- Telemetry and control of distributed energy resources (PV, batteries, thermostats...)
- "Smart grids" software
Strong technical and business communication skills
- Demonstrated ability to communicate and interact with business and technical competencies without supervision
- Demonstrated ability to translate very technical knowledge into business terms
- Demonstrated ability to design and build communication tools that can “tell the story” in a manner that relates to business value and practical consequences
Basic training and experience
- 5+ years of experience as a Data Scientist or in algorithms development
- Master’s degree in computer science, operations research, statistics or a similar field
- Working experience in the utility/energy industry
- Active github repository and stackoverflow account
- Data science competition entries (kaggle, kdnuggets, etc.)
- Experience working with and creating data architectures.
- Experience with big data frameworks, e.g. Hadoop, Map/Reduce, Spark...
- Strong Business Analytics background:
- -NPV, DCF
- -Cost Benefit Analysis
- PhD degree in a quantitative discipline
Language requirements: Fluency in English. Any other languages of benefit.
You would be working for a Silicon Valley start-up, with the backing of a major global energy company, whose product is already the leading Electric Vehicle smart charging & integrated solutions provider in the USA and is fast becoming Europe’s number one provider.