Data Scientist – Machine Learning/Deep Learning – ZoomCar

Responsibilities:

• Develop and deploy personification algorithms to enhance user experience, collaborating with cross-functional teams to integrate personalized features into the platform.
• Collaborate closely with product and business teams to devise data-driven solutions and statistical models.
• Participate in proof of concepts and experiments to demonstrate the value of machine learning, aligning with product requirements.
• Continuously monitor business and product metrics, fine-tuning existing models to adapt to changing market trends and needs.
• Identify, assess, and interpret trends or patterns within extensive and complex datasets.
• Utilize insights to generate actionable recommendations for improving product features and overall business strategies.
• Build predictive models to forecast user behaviors, demand, and market trends, optimizing pricing strategies, cataloging, and recommendations across various user touchpoints.
• Collaborate with engineering teams to deploy scalable and reproducible data science models.
• Define appropriate testing environments, including A/B testing methodologies, and establish accurate metrics for tracking new features and product performance.
• Collaborate with the analytics team to construct precise measurement frameworks for evaluating outcomes effectively.

Requirements:

• B.Tech. / BE in Computer Science, IT, or Electronics (Circuit Branches) or a Bachelor’s/Master’s degree in Data Science/ML or a similar field.
• Strong hands-on experience in Python and SQL.
• 2-4 years of experience in developing predictive models using statistical, machine learning, and deep learning algorithms.
• Ability to think creatively to solve complex business problems using data-driven solutions, with strong problem-solving and analytical skills.
• Understanding of different databases and stream processing tools like Kafka and queues.
• Proficiency in Linux fundamentals and ability to understand basic performance metrics such as CPU, memory, and network latency.
• Hands-on experience with frameworks like Flask, FastAPI, Django, NumPy, and Pandas.
• Familiarity with analytical tools such as Tableau and Excel is an added advantage.
• Knowledge of cloud-based solutions, preferably AWS services like Lambda, SQS, EKS, etc.