Provide meaningful insight on how to improve current business operations.
Requirements
- Experience in building and deploying predictive models and scalable data pipelines
- Demonstrable experience with common data science toolkits, such as Python, PySpark, R, Weka, NumPy, Pandas, scikit-learn, SpaCy/Gensim/NLTK etc.
- Knowledge of data warehousing concepts like ETL, dimensional modeling, and sematic/reporting layer design.
- Knowledge of emerging technologies such as columnar and NoSQL databases, predictive analytics, and unstructured data.
- Fluency in data science, analytics tools, and a selection of machine learning methods – Clustering, Regression, Decision Trees, Time Series Analysis, Natural Language Processing.
- Strong understanding of data governance/management concepts and practices.
- Strong background in systems development, including an understanding of project management methodologies and the development lifecycle.
Responsibilities
- Design, build, tune, and deploy divisional AI/ML tools that meet the agreed upon functional and non-functional requirements within the framework established by the Enterprise IT and IS departments.
- Perform large scale experimentation to identify hidden relationships between different data sets and engineer new features
- Determine requirements that will be used to train and evolve deep learning models and algorithms
- Visualize information and develop engaging dashboards on the results of data analysis.
- Build reports and advanced dashboards to tell stories with the data.
- Proactively mine data to identify trends and patterns and generate insights for business units and management.
- Identify technical areas for improvement and present detailed business cases for improvements or new areas of opportunities.
Other
- Work closely with domain experts and SME’s to understand the business problem or opportunity and assess the potential of machine learning to enable accelerated performance improvements
- Communicate model performance & results & tradeoffs to stake holders
- Lead, develop and deliver divisional strategies to demonstrate the: what, why and how of delivering AI/ML business outcomes
- Build and deploy divisional AI strategy and roadmaps that enable long-term success for the organization that aligned with the Enterprise AI strategy.
- Mentor other stakeholders to grow in their expertise, particularly in AI / ML, and taking an active leadership role in divisional executive forums
