Data Scientist / Machine Learning Engineer

Role:

1. End-to-End Model Development

• Design and implement ML pipelinesfrom 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.

1. 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).

1. 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.

1. 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.

1. 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.

1. 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.

1. 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 withNLP,computer vision, orrecommendation systems.
• Exposure toMLOpstools and platforms for end-to-end model lifecycle management.
• Contributions to open-source AI/ML projects or research publications.
• Background inmathematicsorstatisticsfor rigorous model validation.