- Maintain clear and consistent communication, both verbal and written, to understand data needs and report results
- Create clear reports that tell compelling stories about how customers or clients are doing business with the company
- Assess the effectiveness of data sources and data collection techniques and improve data collection methods
- Conduct research from which you will develop prototypes and proof of concepts
- Establish new systems and processes and seek opportunities to improve data flow
- Assess new and emerging technologies
- Represent the company at external events and conferences
- Build and develop relationships with clients
- Identify valuable data sources and automate collection processes
- Undertake pre-processing of structured and unstructured data
- Analyze large amounts of information to discover trends and patterns
- Build predictive models and machine learning algorithms
- Combine models with assembly modeling.
- Work with stakeholders across the organization to identify opportunities to leverage enterprise data to drive business solutions.
- Extract and analyze data from company databases to optimize and improve product development, marketing techniques and business strategies.
- Assess the effectiveness and accuracy of new data sources and data collection techniques.
- Develop custom data models and algorithms to apply to datasets.
- Use predictive modeling to augment and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
- Develop the company’s A/B testing framework and test the quality of the model.
- Coordinate with different functional teams to implement models and monitor results.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
Live
- 5-7 years of experience manipulating datasets and creating statistical models
- Strong problem-solving skills with a focus on product development
- Experience using statistical computer languages to manipulate data and derive insights from large data sets
- Experience working and building data architectures
- Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their actual advantages/disadvantages
- Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical testing and proper usage, etc.) and experience with applications
- Excellent written and verbal communication skills for coordination between teams
- Knowledge and experience of statistical and data mining techniques
- Experience in querying databases and using statistical computer languages
- Experience using web services
- Experience building and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
- Experience analyzing data from third-party vendors
- Experience with distributed data/computing tools
- Experience in visualizing/presenting data for stakeholders
Qualifications A diploma, master’s or doctoral degree in statistics, mathematics, computer science or another quantitative field, with the following subjects:
- Computing
- Data Science/Computer Science and Data Science
- Engineering
- Mathematics and operations research
- Physics
Technical skills
- Statistical and computer analysis
- machine learning
- deep learning
- Processing large data sets
- Data visualization
- Data Conflict
- Math
- Programming
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