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.

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.)

The Volvo Group drives prosperity through transport solutions, offering trucks, buses, construction equipment, power solutions for marine and industrial applications, financing and services that increase our customers’ uptime and productivity. Founded in 1927, the Volvo Group is committed to shaping the future landscape of sustainable transport and infrastructure solutions. Countless career opportunities are offered across the group’s leading brands and entities that share a culture of Trust, Passion, High Performance, Change and Customer Success. 

Working at Volvo Energy, you are here to make a fundamental break through and propel electrification, from development of charging and infrastructure solutions to monitor and optimize, refurbish/remanufacture, reuse/repurpose and recycle state-of-the-art batteries that power our Volvo Group products. Joining us means working in a global context with people from diverse backgrounds and different nationalities while contributing to the core business and results of Volvo Group. We are goal oriented and business focused people who believe that the best results are achieved while having fun and when we challenge each other. Sustainability and diversity in our DNA, since Volvo Energy was created in April 2021. The story continues here. Join us and work where the action is!

Vi vill lära känna dig



Resan har startat! Du kommer att motta en bekräftelse via email så snart du har skickat din ansökan. Du kan fortsatt uppdatera din personliga profil genom att logga in på ditt konto. Det rekryterande teamet kommer nu att titta på din ansökan tillsammans med den rekryterande chefen. Utvalda kandidater kommer därefter att kontaktas med information om nästa steg.


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