Data Scientist

Data Inc
2 days ago

Role details

Contract type
Permanent contract
Employment type
Part-time (≤ 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Intermediate

Job location

Remote

Tech stack

Data analysis
Computer Programming
Databases
Data Visualization
Database Queries
Python
Machine Learning
Power BI
SciPy
Tableau
PyTorch
Keras
Scikit Learn
Information Technology
Data Analytics
Feature Selection
Unsupervised Learning

Job description

  • Develop data analysis pipelines to interpret vehicle telemetry and battery usage patterns across various vehicle applications.
  • Design, build, validate, and maintain predictive models that deliver battery health insights for connected battery solutions.
  • Analyze machine and production-line data to better understand manufacturing processes and operational performance.
  • Develop and maintain ML/statistical models aimed at:
  • improving production throughput,
  • reducing scrap rates,
  • enhancing product quality,
  • and optimizing manufacturing efficiency.
  • Collaborate closely with data scientists, engineers, and business stakeholders to design effective, data-driven solutions.
  • Communicate analytical findings and decision-making processes to both technical and non-technical audiences.
  • Support cross-functional teams across the organization with machine learning and statistical modeling expertise.

Requirements

  • Bachelor's degree in Statistics, Mathematics, Computer Science, Engineering, or a related technical field.
  • 3+ years of professional experience in data science, machine learning, or applied statistics, or equivalent academic research experience through a Master's or PhD program.
  • Strong programming skills in Python, Julia, or R, including experience with ML/statistical libraries such as:
  • Scikit-learn,
  • SciPy,
  • Statsmodels,
  • PyTorch,
  • and Keras.
  • Experience using BI and visualization tools such as Power BI or Tableau.
  • Strong knowledge of machine learning methods including:
  • supervised and unsupervised learning,
  • feature selection,
  • dimensionality reduction,
  • regression,
  • classification,
  • clustering,
  • and time-series analysis.
  • Experience working with databases and writing SQL queries.
  • Hands-on experience developing, training, validating, and deploying ML/statistical models.

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