Senior Data Scientist

Accountabilities

In this role, you will lead the development and deployment of advanced machine learning solutions, combining research-driven innovation with strong engineering practices. Your responsibilities will include:

• Design, build, and deploy production-grade machine learning systems, taking solutions from initial prototypes through to production environments.

• Improve the reliability, accuracy, scalability, and maintainability of existing machine learning services.

• Research and implement innovative AI and machine learning approaches, staying up to date with emerging technologies, methodologies, and scientific developments.

• Develop machine learning solutions for AIOps and IT operations using large-scale operational datasets.

• Translate research findings and scientific publications into practical, production-ready implementations.

• Design and optimize machine learning models, including model selection, feature engineering, and performance optimization strategies.

• Apply statistical modeling, time-series analysis, anomaly detection, and other advanced machine learning techniques to solve complex operational challenges.

• Collaborate with product managers, data scientists, engineering teams, and other stakeholders to align AI/ML solutions with business and product objectives.

• Work closely with AI Systems Technical Leads to deliver optimized AI systems and services into production.

• Communicate technical findings, insights, and recommendations clearly to both technical and non-technical stakeholders.

• Document research, development processes, model performance, and implementation decisions.

• Establish and maintain best practices for ML experimentation, model versioning, reproducibility, testing, and deployment.

• Take ownership of production system performance, proactively identifying technical issues and implementing effective solutions.

• Guide engineering decisions and mentor other team members, promoting high standards in system design, code quality, and AI-assisted development.

• Identify technical debt, scalability challenges, and opportunities for continuous improvement across machine learning systems.

Requirements:

The ideal candidate is an experienced machine learning professional with strong research capabilities, practical software engineering expertise, and a passion for bringing advanced AI solutions into production. You should bring:

• A Bachelor's degree in Computer Science, Engineering, Mathematics, or a related discipline. An advanced degree in AI, Machine Learning, or a related field is preferred.

• At least 4 years of experience in machine learning engineering, with demonstrated advanced development or research capabilities through innovative projects, publications, or academic theses.

• Strong understanding of statistical modeling, machine learning fundamentals, and time-series analysis. Knowledge of deep learning is an advantage.

• Advanced Python programming skills and experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, and Hugging Face.

• Hands-on experience with unsupervised learning and clustering techniques, including applications involving unstructured text data.

• Experience with anomaly detection and time-series forecasting using approaches such as Prophet, VAR, and deep learning models.

• Working knowledge of causal inference and root-cause analysis methodologies.

• Ability to interpret scientific research papers and translate theoretical approaches into practical implementations.

• Strong software engineering fundamentals, including testing, version control, code reviews, and maintainable code development.

• Experience building and operating production machine learning services involving APIs, streaming data, SQL, containers, and cloud environments.

• Essential experience in Natural Language Processing (NLP).

• Familiarity with machine learning experiment tracking, model versioning, and reproducible research practices.

• Research publications or open-source contributions in machine learning are preferred.

• Previous experience in AIOps, observability, or IT Service Management (ITSM) is an advantage.

• Excellent collaboration and communication skills, with the ability to work effectively across product, data science, and engineering teams.

• Strong analytical thinking and problem-solving abilities, particularly when diagnosing complex production issues.

• A strong sense of ownership and accountability, with a focus on system reliability, performance, and operational excellence.

• Technical leadership and mentoring capabilities, with the ability to guide colleagues and promote engineering best practices.

• A continuous improvement mindset and proactive approach to identifying technical challenges and innovative solutions.

Benefits:

• Full-time employment opportunity.

• Opportunity to contribute to cutting-edge AI and machine learning research and product development.

• Hands-on involvement in taking innovative machine learning solutions from research and experimentation through to production.

• Exposure to advanced AI technologies, intelligent automation, and enterprise-scale applications.

• Opportunity to work on complex AIOps and IT operations challenges using large-scale datasets.

• Collaboration with experienced engineering leaders and multidisciplinary product and technology teams.

• Opportunities to demonstrate technical leadership, mentor colleagues, and influence engineering decisions.

• Professional growth through continuous exposure to emerging AI/ML research, tools, and methodologies.

• Opportunity to contribute to innovative enterprise AI solutions with measurable real-world impact.