Senior Data Scientist

Your key responsibilities will include:

• working with business partners to identify and refine use cases
• applying machine learning and Generative AI techniques to prospect for business insights and find patterns in data which could be valuable for the business.
• partnering with data and platform engineers to deploy models and dependencies such as data pipelines
• leveraging generative AI capabilities and applying solutions to address complex business problems
• creating and communicating insights, through appropriate presentation and visualization techniques
• staying abreast of the latest appropriate analytical techniques and recommending these where needed
• supporting business management in all matters concerning analytical techniques.
• supporting the business transformation of the enterprise through analytics.
• leading and conducting deep-dive research and analyses to identify opportunities

Preferred Background:

• Bachelor’s or Master’s degree in Computer Science, Applied Mathematics, Quantitative Economics, Statistics, or related field. 6 additional years of related experience beyond the minimum required may be substituted in lieu of a degree.
• 6+years of related experience in data analysis, predictive modeling
• proficient level of business acumen in the areas of the business operations, industry practices and emerging trends.

· Strong proficiency in machine learning algorithms, particularly deep learning techniques relevant to generative models (GANs, VAEs, Transformers)

· Expertise in Python programming language and related data science libraries

· Knowledge of cloud computing platforms (AWS, Azure, GCP) for scalable model training and deployment

· Experience and knowledge of ML ops and AI Ops.

• knowledge of Data Science principals and experience with methodologies including classical data science and generative AI.
• experience with any one of the following statistical and predictive modeling approaches: Linear Regression; Logistic Regression; Clustering; Embedding Models; Neural Networks.
• experience with at least two of the following areas: classical machine learning, deep learning, natural language processing, probability theory, statistics, causal inference, time series forecasting
• experience with an analysis tools such as SQL and R or Python for data analysis