Data Scientist

Experience:

Location:

Department:

Employment Type:

Immediate joiners

Job Summary:

Data Scientist

predictive maintenance

Mechanical Engineering

Key Responsibilities

• Develop and deploy

  machine learning models

  for a variety of use cases such as predictive maintenance, quality monitoring, and operational optimization.
• Design and implement models using

  video and image data

  for tasks like visual inspection, object detection, defect identification, or process automation.
• Analyse large and complex datasets from diverse sources — including sensor data, logs, time-series, and multimedia (images/video) — to extract actionable insights.
• Explore and apply Generative AI techniques where applicable to enhance automation, analysis, or content generation in engineering and operational contexts.
• Build and maintain scalable

  data pipelines

  and ensure reliable data preprocessing, integration, and transformation for analysis.
• Collaborate closely with cross-functional teams including engineering, manufacturing, product, and software to identify opportunities for data-driven improvements.
• Present insights and model outputs through

  dashboards

  , reports, and visualizations using tools such as Power BI, Tableau, or Python libraries (e.g., Matplotlib, Plotly).
• Evaluate model performance, conduct A/B testing where applicable, and ensure continuous improvement of deployed solutions.
• Maintain best practices in code quality, documentation, and model interpretability to ensure robustness and transparency.
• Stay current with advances in data science, computer vision, and AI, and assess their potential application in solving business or engineering problems.

Required Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, or a related quantitative field.
• 2 to 3 years of hands-on experience in data science, machine learning, or analytics roles.
• Strong proficiency in Python, with experience using libraries such as Pandas, NumPy, and Scikit-learn.
• Experience with deep learning frameworks like TensorFlow or PyTorch.
• Familiarity with computer vision libraries such as OpenCV for working with image or video data.
• Familiarity with Generative AI frameworks or tools (e.g., OpenAI, Hugging Face Transformers) and a basic understanding of their practical applications.
• Solid understanding of machine learning concepts, including model development, validation, and evaluation techniques.
• Proficiency in SQL and NoSQL for querying and manipulating data.
• Experience working with large and diverse datasets, including preprocessing, cleaning, and transformation.
• Strong data visualization skills using tools such as Matplotlib, Seaborn, Plotly, Power BI, or Tableau.
• Familiarity with version control systems like Git and collaborative coding practices.
• Ability to interpret complex data and communicate findings clearly to both technical and non-technical audiences.
• Strong problem-solving skills and a structured approach to analytical thinking.

Preferred Qualifications

• Experience working with sensor data, time-series data, or datasets from engineering or industrial systems.
• Understanding of physical systems, process operations, or product lifecycle data is a plus.
• Familiarity with cloud platforms such as AWS, Azure, or GCP, and big data tools like Spark or Hadoop.
• Exposure to domains such as predictive maintenance, manufacturing analytics, or process optimization.
• Knowledge of IoT ecosystems is advantageous.
• Experience in deploying models or analytics solutions in production environments.
• Familiarity with MLOps practices, including model tracking, versioning, and reproducibility.
• Working knowledge of containerization tools like Docker is an added advantage.

Why Join Us?

• Tackle real-world challenges at the intersection of

  engineering and data science

  , with tangible impact on products and operations.
• Collaborate with cross-functional teams of

  engineers, data scientists, and domain experts

  in a dynamic and supportive environment.
• Join a culture that values

  innovation, experimentation

  , and continuous improvement.
• Benefit from structured

  mentorship, training programs

  , and clear paths for

  career development

  .
• Enjoy

  competitive compensation

  , access to industry-leading tools, and a

  flexible work environment

  that supports work–life balance.