Data Scientist

We are looking for a Data Scientist to join the Piaxo team. This role is for our client based in the United States, operating in the battery development industry.

Data Scientist will be responsible for developing battery health algorithms that are fed by real time vehicle data using state-of-the-art statistical and ML techniques. He/she will also support current initiatives to improve core processes in manufacturing and quality using data collected at the machine level in all our plants. From designing systems that monitor data streams to send signals and alerts to customers, to adding machine learning pipelines that enhance existing processes.

About Piaxo

Piaxo is an engineering company delivering end-to-end R&D services for the automotive industry, covering the complete V-Cycle from concept to validation. We stand out through senior engineering teams, a strong focus on quality and processes, and proven delivery against key milestones such as ASPICE compliance, supported by TISAX Level 3 certification and our role as an official Tehnica Engineering reseller.

Headquartered in Romania, with offices in Cluj-Napoca and Timișoara and an international presence in Munich, Germany, Piaxo is expanding its Timișoara site into a permanent office with an automotive laboratory. The location is planned to scale to 120+ engineers and technical experts, delivering complete product development across all competencies in line with ASPICE 4.0, as part of a long-term, well-defined program developed with a major Tier-1 customer, offering stability, technical depth, and long-term career continuity.

Responsibilities:

Key Responsibilities

• Build data analysis pipelines to understand vehicle signals related to energy consumption and component performance across different vehicle applications.
• Build, develop, validate, and maintain predictive models that assess the health and performance of connected powertrain or energy systems.
• Build data analysis pipelines to understand machine signals related to product manufacturing and assembly processes.
• Build, develop, validate, and maintain optimization models to improve production throughput, reduce scrap rates, and enhance overall product quality.
• Collaborate closely with other data scientists, engineers, and business stakeholders to craft solutions.
• Communicate details of the data-driven decision process to non-technical stakeholders.
• Provide ML and statistical support to other areas of the organization to enable data-informed decision-making across teams.

Required Qualifications

• Bachelor’s Degree in Statistics, Mathematics, Computer Science, or a related engineering field.
• 3+ years of professional experience in data science, machine learning, or a related field (or equivalent academic experience through a Master’s or Ph.D. program).
• Strong programming skills in Python, Julia, or R, and ML/statistical packages such as Scikit-learn, SciPy, Statsmodels, PyTorch, Keras.
• Experience with Power BI or Tableau.
• Good understanding of probability, statistics (hypothesis testing, design of experiments, power calculations, mixed-effect models), linear algebra, and mathematical bases of machine learning methods.
• Good understanding of machine learning techniques for supervised/unsupervised learning, feature selection, dimensionality reduction, regression, classification clustering, and time series analysis.
• Experience interacting with databases and writing SQL queries.
• Experience developing, training, validating, and deploying ML/statistical models.
• Experience using data visualization techniques for data analysis and model explanation.
• Experience effectively collaborating with non-technical stakeholders.
• Candidates must be self-driven, curious, and creative. They must demonstrate the ability to work in diverse, cross-functional teams in a dynamic business environment.

Preferred Qualifications

• Experience and understanding of different deep learning architectures and how each of them can be applied to different problems.
• Experience with big data technologies such as Databricks, Spark, Snowpark, etc.
• Experience with cloud technologies such as Microsoft Azure, AWS.
• Experience deploying machine learning solutions for production (e.g., REST APIs in Azure ML, Docker, Kubernetes) in large data science projects.
• Experience mentoring Analysts and/or Data Scientists.
• Experience with any of the following: casual inference, probabilistic graphical models, Bayesian statistics, probabilistic programming languages (Stan) and/or packages (PyMC, PyStan) is a plus.
• Experience working in manufacturing, IoT, vehicle data, and/or battery engineering is a plus.
• Publications in peer-reviewed journals and/or conferences related to statistics, mathematics, machine learning is a plus.