Senior Data Scientist

Key Responsibilities:

• End-to-End Model Development: Design, develop, and implement machine learning models across various domains, including data preprocessing, feature engineering, model training, validation, and evaluation.
• Enterprise-Grade Applications: Focus on building robust, scalable, and reliable machine learning applications that can be deployed and maintained in an enterprise environment.
• Cloud Deployment & Management: Deploy and manage machine learning models in production environments, utilizing cloud platforms like Microsoft Azure (Azure Machine Learning, Databricks) or AWS (SageMaker, Glue). Experience in MLOps is a plus.
• LLMs and Generative AI: Leverage Large Language Models and Generative AI techniques to enhance our data-driven products, including but not limited to fine- tuning, prompt engineering, and applying generative models for value extraction.
• Data Analysis and Interpretation: Conduct in-depth data analysis, identify key patterns and insights, and communicate findings to support data-driven business decisions and strategies.
• Business Understanding: Develop a deep understanding of business requirements and objectives, translating them into actionable data science projects, and demonstrating how the work impacts business outcomes.
• Cross-Functional Collaboration: Work closely with product managers, engineers, and other stakeholders to gather requirements, align on objectives, and deliver high- quality solutions.
• Mentorship and Guidance: Mentor junior data scientists, sharing best practices and methodologies in data science and machine learning.
• Hypothesis Development: Develop and test hypotheses to improve existing models, contributing to the creation of new solutions.
• Technology Research: Continuously research and evaluate new trends and technologies in data science and machine learning to improve the capabilities of the department.

Required Qualifications:

• Master’s degree in Data Science, Computer Science, Statistics, or a related field.
• Minimum of 4+ years of professional experience in developing, deploying, and managing machine learning models in production environments, with a focus on building scalable applications.
• Deep understanding of statistical concepts and machine learning algorithms, including their theoretical foundations and practical implementations.
• Proficiency in Python with proven hands-on experience using libraries such as pandas, scikit-learn, TensorFlow, or PyTorch.
• Practical experience with cloud platforms, specifically Azure Machine Learning or AWS SageMaker, including deploying models and managing pipelines is a plus.
• Experience in using data visualization tools like Power BI or Tableau to present complex data findings clearly and concisely.
• Proven analytical and problem-solving skills, with a track record of delivering actionable insights aligned with business needs.
• Excellent communication skills, with the ability to clearly articulate technical concepts to both technical and non-technical stakeholders, and to convey the business impact of data driven work.
• Demonstrated ability to manage projects independently, balance multiple priorities, and deliver results within deadlines.
• Ability to learn and apply new technologies quickly, specifically in the field of Large Language Models and Generative AI, including but not limited to fine tuning pre trained models, prompt engineering and application of generative models to solve business problems.

Preferred Qualifications:

• Experience in building enterprise-grade machine learning applications, with a focus on reliability and scalability.
• Hands-on experience with a variety of pre-trained open-sourced and closed-sourced LLMs such as Llama, OpenAI, and others.
• Experience in working with MLOps tools and frameworks and in creating and implementing automated CI/CD pipelines for machine learning models.