Battery Analyst

Key Responsibilities:
Your role is to support in development and execution of analytical and data driven solution for battery management in Volvo Energy focusing on battery second life.
You will understand and gather insights from battery usage patterns during the complete life cycle in first life and support development of algorithms for optimizing battery health and risk mitigation in first life and second life.

Qualifications:
Bachelor’s/Master’s degree in Applied Statistics/Mathematics/Engineering or another equivalent field with good analytics and problem-solving skills.
Total experience of 4+ year in engineering or related field and 2+ years of experience in data science and machine learning.
Experience in engineering tools such as COMSOL, ANSYS etc is highly desirable.
Experience on simulation software’s such as MATLAB, AnyLogic, Simulia etc is add-on.
Willingness to explore and understand about Li-ion battery technology is must.

Work Experience:
Candidate should have hands-on experience in ML project workflows: data ingestion, data preprocessing, feature engineering, model building and model validation. Prior experience in deployment, productionizing, monitoring and retraining machine learning models is a plus.
Experience in the field of PHM (Prognostics and Health Management), FDD (Fault detection and diagnostics) and Predictive maintenance is highly preferred.
Working knowledge on at least one cloud platforms: Azure is preferred.
Experience with Business Intelligence tools: PowerBI is preferred.
Working knowledge on relational and non-relational databases like SQL, MySQL, MongoDB etc. good to have.
Big data technology experience (Spark, Hadoop, Hive SQL) is plus
Familiarity with ML frameworks like Tensorflow, Pyspark, Keras, Databricks etc
Digital twin, surrogate modeling and reduced order modeling is plus.

Technical Strengths:
Good command in programming languages such as Python or R. In depth knowledge of python libraries like NumPy, SciKit, Pandas, Matplotlib, Seaborn etc. is essential.
Good experience in data exploration, data visualization and pattern recognition techniques.
Conceptual and hands on experience in machine learning algorithms: Regression, Classification, Tree-based algorithms (Decision Tree, Random Forest, XGBoost, LGBM), Clustering, Time Series Analysis and Deep learning techniques (ANN, CNN, RNN etc.)