Data Scientist | Machine Learning Engineer
1.1. Requirements: Forms strong relationships with all parts of the team: marketing, electrical, controls, power electronics, power systems, electronic, firmware, mechanical, service, compliance, quality, reliability and manufacturing. Works with the stakeholders to understand technical requirement and how the relate to business drivers. Creates and maintains functional requirement documentation and traceability between software functional requirements and “top level” system requirements.
1.2. Architect: Utilize a systematic, architected approach to your design such as conceptual/system modelling, object oriented programming, multi-level protocol stack, multi-layer program breakdown, functions, modules, libraries, and top level applications.
1.3. Analysis, Modelling & Algorithms: Research, diagnosis, review and selection of the appropriate techniques for a modelling, analysis, algorithm creation and machine learning. Works to ensure the selecting optimal features, building and optimizing classifiers using machine learning techniques. Implements natively in the operating environment wherever possible.
1.4. Data Preparation: creating, evolving and maintaining a data integrity system via processing, cleansing and verifying the data used for analysis in an ad-hoc and via an autotomized system. Build/Maintain APIs/Tools to allow technical non-experts to access datasets and utilize them.
1.5. Test & Confidence: Define, create, execute, and report on algorithm/model testing. Debug and tune models/algorithms based on test results and field data. Be able clearly express confidence, statistically and experientially, in your analysis, modeling and algorithms.
1.6. Data Visualization: Accurately create simple abstractions to communicate the story of the data and the hardening and resilience of the analysis technical and non-technical audiences. Ensure efficient and effective interactions for the team members using the visualizations on a daily basis.
1.7. Collaboration & Documentation: Analyze, simulate and program within a collaborative, shared environment. Create design documentation, work instructions, and other technical documentation to support design efforts and communication to other designers.
1.8. Continuous Improvement: Provide input, strongly support, and create design guideline documentation driving forward the continuous improvement of the team technologies, practices, process, and methods and overall quality of deliverables.
1.Education and Experience
- Masters or PhD degree in Applied Mathematics, Physics, Software Engineering, Computer Engineering, Electrical Engineering, Mechatronics/Systems Engineering, Engineering Physics, Econometrics, Financial Engineering, Financial Risk Management, or equivalent
- If holding a Graduate Degree in Econometrics, or quantitate Financial domain then Bachelor’s Degree in Physics or the above mentioned Engineering disciplines is required.
- 3+ years proven experience as a data scientist, machine learning engineer or equivalent experience.
2. Knowledge, Skills and Abilities
- Proficiency with
o languages such as: MATLAB, Mathmatica, C, SQL, Python, R, or equivalent
o NoSQL databases such as Azure, Cassandra, MongoDB
o data visualization tools, such as Tableau, Qlik, or equivalent
- General mechatronic competency (electrical, power, sensors, electronics controls, software, embedded firmware, communications).
- Proficiency in Machine Learning, Conceptual modelling, Predictive modelling, Model Implementation, Distributions, regression testing, statistical testing, confidence, sensitivity, covariance. Ad-hoc analysis and presenting results in a clear manner
- High levels of initiative and resourcefulness. Capable of operating with minimal direction.
- Excellent organizational skills, documentation ability, attention to detail
- Goal oriented; ability to maintain the greater picture while operating in the details
- Strong communication and interpersonal skills with demonstrated collaborative teamwork capabilities
- Structured approach to troubleshooting and problem solving