Data Scientist / Machine Learning Engineer

1. End-to-End Model Development

   • Design and implement ML pipelines from data collection and preprocessing to training, validation, and deployment.
   • Select the right models (classical ML or deep learning) and optimize them for performance and scalability.

2. Advanced Coding & Software Engineering

   • Write clean, efficient, and maintainable code in Python (or relevant languages like R, Scala, C++).
   • Utilize ML frameworks (TensorFlow, PyTorch, scikit-learn, etc.) and adhere to software engineering best practices (version control, CI/CD).

3. Research & Innovation

   • Stay updated with the latest AI/ML research, frameworks, and tools; propose and test new ideas or methodologies.
   • Conduct experiments, benchmark against industry standards, and publish or present findings as needed.

4. Data Exploration & Feature Engineering

   • Analyze large datasets for insights; engineer relevant features and conduct appropriate transformations.
   • Collaborate with data engineering teams to ensure data pipelines are robust and scalable.

5. Collaboration & Mentorship

   • Work cross-functionally with product managers, software engineers, and domain experts to integrate ML solutions into products.
   • Share knowledge, review code, and assist junior team members in their growth and development.

6. Deployment & Monitoring

   • Implement MLOps best practices, including containerization (Docker), orchestration (Kubernetes), and cloud services (AWS, Azure, GCP).
   • Monitor model performance in production; iterate and refine models to adapt to evolving data and requirements.

7. Problem-Solving & Performance Optimization

   • Tackle complex data challenges, optimize algorithms for latency and throughput, and ensure reliable system performance at scale.
   • Identify and address data quality issues, model drift, and other factors affecting solution success.

Required Qualifications

• Education & Experience

  • Bachelor s or Master s degree in Computer Science, Data Science, Mathematics, Engineering, or a related field. Advanced degrees are a plus.
  • Proven track record (2+ years) in developing and deploying AI/ML solutions. (Adjust experience level as needed.)

• Technical Expertise

  • Comprehensive AI/ML Knowledge

    : Classical ML (regression, classification, clustering, etc.) and deep learning (CNNs, RNNs, Transformers).

  • Strong Coding Skills

    : Fluency in Python; familiarity with other programming languages is a bonus.

  • R&D Mindset

    : Hands-on experience with research methodologies, ability to quickly prototype and evaluate new ideas.

  • Frameworks & Tools

    : Proficiency in ML/DL libraries (TensorFlow, PyTorch, scikit-learn), data platforms (Spark, Hadoop) is beneficial.

  • Cloud & Deployment

    : Experience with cloud platforms (AWS, Azure, GCP) and container orchestration (Docker, Kubernetes).

• Soft Skills

  • Excellent problem-solving ability and a hunger for tackling complex, ambiguous challenges.
  • Strong communication skills, capable of articulating technical concepts to a diverse audience.
  • Self-motivated with a drive to continuously learn and adopt new AI technologies.

Preferred/Bonus Skills

• Experience with

  NLP

  ,

  computer vision

  , or

  recommendation systems

  .
• Exposure to

  MLOps

  tools and platforms for end-to-end model lifecycle management.
• Contributions to open-source AI/ML projects or research publications.
• Background in

  mathematics

  or

  statistics

  for rigorous model validation.