As a Data Scientist at IBM, you will help transform our clients’ data into tangible business value by analyzing information, communicating outcomes and collaborating on product development. Work with Best in Class open source and visual tools, along with the most flexible and scalable deployment options. Whether it’s investigating patient trends or weather patterns, you will work to solve real world problems for the industries transforming how we live.
- Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions
- Formulate overall analytic solutions to address the identified business objectives and pain points
- Assess the effectiveness and accuracy of new data sources and data gathering techniques.
- Extract, clean, and transform structured and unstructured data to make it consumable for downstream analytics
- Perform statistical analysis, data mining and machine learning modelling on massive datasets to increase and optimize customer experiences, revenue generation and other business outcomes.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
- Previous working/research experience for 3-5 years and recently worked as Data Scientist/Data Analyst with Bachelor’s degree in Statistics, Data Science, Mathematics, Computer Science, Econometrics or relevant quantitative field. Master’s or PhD degree is preferred
- Hands on experience with Python and SQL
- Good knowledge of statistical concepts (properties of distributions, statistical tests) and machine learning techniques (such as regression, classification, time series, clustering, collaborative filtering, NLP) and their real-world advantages/drawbacks
- Good knowledge of at least one of the key visualization libraries (such as matplotlib, seaborn, plotly, ggplot2, Shiny, bokeh)
- Strong analytical and problem-solving skills
- A drive to learn and master new technologies and techniques
- Excellent written and verbal communication skills for coordinating across teams
- Having end-to-end analytic project experience from requirement gathering, business/data understanding, feature engineering, model development to insight deployment/feedback is a plus
- Hands-on experience in knowledge graph, deep learning, decision optimization and/or MLOps is a plus
- Hands-on experience with distributed data/computing tools such as Spark is a plus