Summary
The job is for a Data Scientist at Data Meaning, a company specializing in Business Intelligence and Data Analytics consulting. The ideal candidate should have experience with either Python or R, Power BI or Tableau, and strong data analysis skills.
Requirements
- Bachelor’s degree in Computer Science, Math or Statistics is preferred
- At least 4 years of professional experience in a data-related role
- Good communication skills (English is a PLUS)
- Knowledge of Statistics and Statistical Modeling
- Understanding of Machine Learning and Predictive Analytics
- Data analysis programming languages – Python and R (at least one)
- Database languages – SQL and NoSQL (desirable)
- Data Visualization – Power BI or Tableau
- Understanding of Big Data infrastructure – Hadoop, MapReduce and Spark
Responsibilities
- Follow consistent practices to ensure data integrity and deal with imperfections in data
- Effectively process structured and semi-structured/unstructured data; proficiently integrate varied datasets
- Work cross-functionally with development and engineering teams
- Analyze large amounts of information to discover trends and patterns, draw conclusions and gain actionable insights
- Build predictive models; propose solutions and strategies for business problems
- Apply the appropriate machine learning algorithm to data problems (e.g. supervised vs. unsupervised machine learning, clustering, ensemble methods, etc.), validate model results
- Work closely with architecture and engineering team to deploy models; communicate results in a clear and non-technical manner
- Interact with senior management and effectively communicate technical information to numerous audiences (e.g. senior management, client community, peers and junior associates)
- Address challenges to analytic processes and influence others to change their approach and work collectively to implement new tools/techniques
- Gain support from related stakeholders for analytic solutions and champion data driven business decisions
- Apply appropriate statistical techniques to data exploration and model development/assessment
- Communicate complex statistical concepts to non-technical audiences; maintain skills through continuing education
- Create meaningful data visualizations to communicate results and highlight business impact
- Effectively collaborate with others to deliver efficient and high-quality code (Python or R)
- Implement solid validation process to ensure consistency and minimize errors in code
- Champion more efficient ways to produce code, maintain skills through continuing education; stay up to date on new technologies/ tools
- Seamlessly transition between languages to apply the right approach to the problem
