Data Scientist-II

Job description

Job Summary
We are seeking a Data Scientist II to join our growing data science team. In this role, you will contribute to a wide range of activities related to end-to-end machine learning (ML) deployments, research, and development of data-driven features that support both internal and external stakeholders.
This position is ideal for someone who enjoys variety, thrives in a collaborative environment, and wants to broaden their expertise in a structured and impactful way.

Job requirements

• Preferred : Prior experience working with weather data or weather-related modeling.
• 3- 5 years of experience building and deploying machine learning models.
• Masters or PhD in statistics, mathematics, computer science, or another quantitative field.
• Strong problem-solving skills with an emphasis on product development.
• Proficient in programming languages such as Python and/or R, with experience using libraries such as pandas, scikit-learn, and TensorFlow.
• Experience with SQL and relational databases for data extraction and manipulation.
• Hands-on experience with ML techniques for predictive modeling, classification, NLP (including large language models), recommendation systems, and time series methods.
• Familiarity with the software development lifecycle, version control (Git), and CI/CD pipelines.
• Experience with cloud-based AI/ML frameworks such as AWS, Azure, or GCP is a plus.
• Strong organizational, communication, and time management skills.
• High level of accountability and ability to deliver results in a fast-paced environment.

Job responsibilities

• Develop subject matter expertise in the organizations data and product ecosystem.
• Partner with cross-functional teams to identify opportunities, design experiments, and deliver scalable ML solutions that create measurable business value.
• Lead the design of experiments and hypothesis testing to support new feature development.
• Design, implement, and deploy machine learning models across diverse use cases, with opportunities to apply techniques to specialized domains such as weather-related modeling.
• Communicate findings and insights clearly to both technical and non-technical audiences using visualizations and presentations.
• Monitor and evaluate model performance and ensure data quality in production.
• Stay current with advancements in machine learning, AI, and emerging technologies.