Senior Data Scientist

About the Role

As a Senior Data Scientist, you will:

1. Engage with stakeholders, business analysts, and project teams to understand data requirements.
2. Work across multiple business domains, including Customer Service, Finance, Sales, and Marketing.
3. Design, develop, and deploy predictive models and machine learning algorithms to address business challenges.
4. Explore, visualize, and prepare diverse datasets for analysis and problem-solving.
5. Build machine learning and statistical models, including generative AI solutions.
6. Apply Natural Language Processing (NLP) techniques to extract insights from text data.
7. Design database models and aggregate data as needed for modeling.
8. Create visualizations and build dashboards in Tableau and/or PowerBI.
9. Extract and present business insights using data storytelling techniques.
10. Analyze large and complex datasets to identify trends and generate meaningful insights.
11. Collaborate with product managers, engineers, and stakeholders to define requirements and deliver solutions.
12. Mentor and guide junior data scientists and analysts.
13. Clearly communicate findings and recommendations to both technical and non-technical audiences.
14. Stay current with the latest data science methodologies, tools, and best practices.
15. Promote the adoption of data science techniques and foster a data-driven culture within the organization.

About You

You are a strong candidate for Senior Data Scientist if:

1. You are a strong candidate for Senior Data Scientist if you have:
2. 6-10 Years of experience in Machine Learning, AI, and Data Science.
3. A degree in a quantitative field (e.g., Computer Science, Statistics) is preferred.
4. Proven technical expertise and business acumen.
5. Proficiency in machine learning, statistical modeling, and generative AI techniques.
6. Advanced skills in Python, SQL & Snowflake
7. Experience with Tableau and/or PowerBI.
8. Hands-on experience with Amazon Web Services (AWS) and SageMaker.
9. Ability to build data pipelines using tools such as Alteryx and AWS Glue.
10. Experience in predictive analytics for customer retention, upsell/cross-sell, customer segmentation, recommendation engines, and building generative AI solutions (e.g., GPT, Llama).
11. Practical knowledge of prompt engineering and working with Large Language Models, Retrieval Augmented Generation (RAG), and AI agents.
12. Experience in business domains such as Customer Service, Finance, Sales, and Marketing.
13. Familiarity with NLP techniques, including feature extraction, word embeddings, topic modeling, sentiment analysis, classification, sequence models, and transfer learning.
14. Knowledge of AWS APIs for machine learning.
15. Strong presentation and data storytelling skills, with the ability to communicate complex results clearly at all organizational levels.