Data Scientist
7 months ago
The Data Scientist will contribute to the creation and evolution of the Data Management Program (DMP), which will include data education, data maturity, policies, standards, data stewardship, data governance, best practices etc.), in order to advance the DMP and continue to evolve Cashco’s data journey.
Some **_Key Responsibilities_** Include but not limited to are:
- Extract, clean, and prepare data from various internal and external sources, such as databases, web analytics, surveys, and reports, for further analysis and modeling.
- Design, develop, and deploy data analysis pipelines and workflows that automate and optimize the data processing and transformation tasks.
- Evaluate and validate the performance, accuracy, and reliability of the models and algorithms, and optimize them for efficiency and scalability.
- Communicate and present the data analysis findings, insights, and recommendations to stakeholders, using clear and compelling visualizations, reports, and stories.
- Collaborate with business users to understand their data problems, questions, and goals, and provide solutions that meet their expectations.
- Research and implement modern data analysis techniques and methodologies.
- Analyze, troubleshoot, and resolve data incidents and provide timely and effective solutions.
- Provide guidance of data sources and services.
- Collaborate with technical team members to deploy and support a data management program.
What **_Competencies_** will help you excel in this role:
- Communication - Practical / Critical Thinking
- Creativity and Innovation - Collaboration
- Professional Development - Personal Integrity
- Decisiveness - Initiative
- Team Focused
**_ Qualifications/Experience Needed:_**
- Bachelor Degree in Computer Science, Computer Engineering, or a related field with a minimum of 5+ years Data Scientist experience is required.
- Proficiency in one or more programming languages such as Python, SQL, R Java, or Scala.
- Experience in working with various data sources, formats, and platforms, such as relational databases, NoSQL databases, APIs, web scraping, cloud computing, or big data frameworks.
- Experience with data analysis techniques and tools, such as data cleaning, preprocessing, exploratory analysis, descriptive and inferential statistics, machine learning, deep learning, natural language processing, computer vision, or recommender systems.
- Ability to create and interpret data visualizations using libraries or software such as Tableau, Matplotlib, Seaborn, Plotly, or Power BI.
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