Rich Products Corporation
Location
Pune | India
Job description
ROLE PURPOSE: The Data Scientist is a multifaceted, full-stack role that demands deep involvement in machine learning, statistical analysis, predictive modeling, and operational deployment. The ideal candidate will play a pivotal role in data discovery, preparation, collection, and integration, serving as a crucial link between our enterprise data analytics team and various business teams. This position requires a unique combination of technical skills, business acumen, and collaborative abilities.
KEY ACCOUNTABILITIES/OUTCOMES: • Machine Learning and Predictive Modeling: Develop advanced machine learning models for predictive analytics. Utilize AI techniques to drive innovative solutions. • Deep Learning and AI Model Development: Build complex AI models, focusing on areas like deep learning, natural language processing, and computer vision. • Data Exploration and Preparation: Perform detailed data analysis and visualization. Engage in exploratory data analysis to answer critical business questions. • Data Collection and Integration: Efficiently handle data collection via APIs and web scraping, ensuring robust database management. • Problem Analysis and Project Management: Lead projects from concept to execution, showcasing strong analytical and problem-solving skills. • Operational Deployment and Integration: Implement models in production environments with a continuous improvement mindset, using technologies such as Azure Databricks, and Azure Machine Learning Service TECHNICAL SKILLS: • Advanced Programming/Coding Proficiency: Strong coding skills and familiarity with software and system engineering design principles, including Test-Driven Development (TDD). • Expertise in Programming Languages and Frameworks: Proficient in one or more popular programming languages and frameworks, such as SQL, Python, Scala, and Spark. Command Line interface expertise is also essential. This document is Confidential • Deep Learning Frameworks: In-depth proficiency in using deep learning frameworks like TensorFlow and PyTorch. • Open-Source and Cloud Technologies: Good understanding of open-source technologies, along with experience in cloud services, particularly Azure. • Quantitative Analysis and Hypothesis Testing: Skilled in testing hypotheses using various quantitative methods and statistical techniques. • A/B Testing on Production Systems: Ability to implement champion/challenger tests (A/B tests) in production environments to evaluate model performance. • Version Control Systems: Experienced in using version control and repository management systems, with a preference for candidates with Git expertise. • Integration of AI Models: Capable of integrating AI and machine learning models into existing applications and systems, ensuring seamless functionality. • Model Tracking and Maintenance: Ability to effectively track, maintain, and govern both experimental and production-ready machine learning models, including monitoring for model drift and performance loss. • MLOps, DevOps, and LLMOps Practices: Proficient in applying best practices in MLOps, DevOps, and LLMOps to ensure continuous robustness, accuracy, and efficiency of models. KNOWLEDGE/SKILLS/EXPERIENCE: A preferred qualification is a bachelor’s or master’s degree in computer science, data science, operations research, statistics, applied mathematics, or a similar quantitative field. Experience and education in comparable areas such as economics, engineering, or physics, is also acceptable. • In-depth knowledge of machine learning, statistical modeling, AI, deep learning, reinforcement learning, NLP, and computer vision. • Proficiency in handling both structured and unstructured data sources. • Ability to conduct scientific experimentation and analysis. • Experience in maintaining production ML models, including monitoring and fairness assessments. • Familiarity with AI ethics and explainability.Job tags
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