We are seeking an experienced Data Scientist to join our team of innovative problem solvers applying advanced machine learning techniques to real-world challenges in the industrial and energy sectors. In this role, you will build and deploy predictive models focused on machine vision, anomaly detection, and predictive maintenance to prevent injuries and equipment breakdowns.

Key Responsibilities:

• Data Exploration & Modeling:
• Analyze large-scale, structured and unstructured datasets to uncover key patterns and insights.
• Build, test, and deploy predictive models using Python-based machine learning libraries (e.g., Pandas, NumPy, Scikit-learn, TensorFlow).
• Develop anomaly detection models and predictive maintenance algorithms to enhance operational efficiency and safety.

Data Engineering & Workflows:

• Build, maintain, and optimize data pipelines and automated workflows—ideally leveraging Databricks or similar platforms (e.g., Azure, AWS, or Apache Spark).
• Handle high-volume sensor data streams with efficiency and scalability.
• Insights Communication & Visualization:
• Use tools such as Matplotlib, Seaborn, or Plotly to visualize results, craft dashboards, and present insights.
• Share findings with clear and concise communication tailored for technical and non-technical audiences.

Key Qualifications:

• Minimum of 3+ years of professional experience as a Data Scientist with a proven track record of delivering impactful solutions.
• Background in working on predictive models related to physical assets (e.g., equipment, machinery) in industrial or energy sectors.
• Hands-on experience solving problems in predictive maintenance, anomaly detection, or other safety/efficiency-related areas.

Technical Proficiency:

• Advanced proficiency in Python and related libraries (e.g., Pandas, NumPy, Scikit-learn).
• Experience in developing workflows in Databricks or similar cloud-based platforms (e.g., Azure, AWS, Spark).