Senior Data Scientist

We are seeking a Good Machine Learning Scientist to join our team. This role is designed for someone who has deep technical expertise in machine learning and can provide leadership and strategic direction for building scalable, secure, and robust machine learning models and systems. The right candidate will lead the design, implementation, and operationalization of large-scale machine learning solutions, while also mentoring team members and collaborating with cross-functional teams to deliver impact.

Responsibilities

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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.