Senior Data Scientist

Key Responsibilities

1. ML & AI Development

• Lead the research, design, and development of advanced machine learning and AI models, ensuring high performance, accuracy, and robustness.

• Develop novel algorithms and architectures, optimizing for real-world deployment constraints such as latency, efficiency, and scalability.

• Leverage cutting-edge advancements in deep learning, generative AI, reinforcement learning, and large-scale ML systems to push the boundaries of AI innovation.

2. Scalable Model Deployment & Optimization

• Build and deploy ML models at scale, ensuring seamless integration into production systems with minimal latency and maximum efficiency.

• Optimize models for performance and efficiency using techniques such as quantization, pruning, distillation, and hardware acceleration (e.g., GPUs, TPUs, FPGAs).

• Drive good practices for A/B testing, model evaluation, and hyperparameter tuning to continuously improve model performance.

3. ML Architecture & Automation

• Design and implement scalable ML architectures that support real-time inference, batch processing, and hybrid AI workflows.

• Develop robust pipelines for data preprocessing, feature engineering, model training, and deployment, ensuring high-quality input data and reproducibility.

• Ensure efficient retraining and model versioning, enabling rapid experimentation and continuous learning in production environments.

4. AI Model Governance, Security & Compliance

• Ensure all ML models adhere to security, privacy, and ethical AI standards, including fairness, explainability, and regulatory compliance.

• Implement techniques for bias detection, adversarial robustness, and secure AI deployment to mitigate risks in real-world applications.

• Establish good practices for model monitoring, drift detection, and performance tracking, ensuring AI systems remain reliable and effective.

5. Domain Understanding, Cross-Functional Collaboration & AI Strategy

• Work closely with product engineering, and product teams to align AI initiatives with business objectives and technical feasibility.

• Influence the broader AI roadmap, advocating for new methodologies, frameworks, and tools to enhance the impact of ML models.

• Communicate complex ML concepts and results to Sr. leadership, product teams, and stakeholders, ensuring alignment on AI strategies and outcomes.

6. Research, Innovation & AI Thought Leadership

• Stay at the forefront of AI and ML research, actively exploring new algorithms, architectures, and applications in deep learning, NLP, CV, and more.

• Lead proof-of-concept (PoC) projects, testing and validating emerging AI technologies for potential production adoption.

• Contribute to AI research communities, publishing papers, attending conferences, and engaging in collaborations with academia and industry partners.

7. Mentorship & AI Talent Development

• Mentor and guide Jr. and mid-level ML scientists, fostering a culture of innovation, experimentation, and continuous learning.

• Lead technical deep dives, AI model reviews, and algorithmic discussions, helping the team stay ahead of industry trends.

• Identify skill gaps and drive AI education initiatives, ensuring the team is proficient in state-of-the-art ML methodologies.

• Bachelors in Computer Science, Machine Learning, Artificial Intelligence, Applied Mathematics, Statistics, or a related field.

• Industry Experience: 10+ years of hands-on experience in designing, developing, and deploying machine learning models at scale in production environments.

• ML & AI Expertise: good theoretical and practical knowledge of supervised and unsupervised learning, deep learning, generative AI, reinforcement learning, probabilistic modeling, and large-scale ML systems.

• Programming & Development Skills: Proficiency in Python with deep expertise in ML frameworks and libraries such as TensorFlow, PyTorch,, Scikit-Learn, Hugging Face, or similar.

• Model Deployment Experience: Experience in deploying and optimizing ML models in cloud-based environments (Azure, AWS, GCP).

• Data Handling & Feature Engineering: Expertise in working with large-scale datasets, time-series data, structured/unstructured data, and applying advanced feature engineering techniques.

• Mathematical & Statistical Proficiency: good foundation in linear algebra, probability, optimization, Bayesian inference, and numerical methods.

• Cross-Functional Collaboration: Ability to work closely with software engineers, product managers, and business stakeholders to translate business needs into AI solutions.

• Communication Skills: Ability to clearly articulate complex ML concepts, write technical reports, and present findings to both technical and non-technical audiences.

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