- Perform in-depth data analysis, including data cleaning, exploration, and visualization, to uncover patterns, trends, and insights from large datasets.
- Develop and implement appropriate statistical and machine learning techniques to make predictions, classify data, and solve specific business challenges.
- Engineer new features and variables from raw data to improve model performance and extract relevant information.
- Evaluate and fine-tune machine learning models using appropriate metrics and validation techniques to ensure robustness and accuracy.
- Collaborate with data engineers to access, transform, and integrate data from various sources, ensuring data quality and consistency.
- Translate data findings into actionable insights and recommendations that contribute to strategic decision-making and business growth.
- Plan and execute controlled experiments, including A/B tests, to assess the impact of changes and interventions.
- Create informative data visualizations and dashboards to communicate findings effectively to both technical and non-technical stakeholders.
- Bachelor’s or Master’s or PhD degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Data Science).
- Proven experience as a Data Scientist, including expertise in data analysis, statistical modeling, and predictive analytics.
- Proficiency in relevant programming languages, for data analysis and model development.
- Strong knowledge of machine learning concepts and the ability to apply them.
- Experience with data visualization tools and libraries for conveying data insights effectively.
- Excellent problem-solving skills and the ability to work independently and as part of a team.
- Strong communication skills to convey complex findings and insights to diverse audiences.
- Familiarity with big data technologies, cloud computing platforms, and database systems.
OTHER KEY CONSIDERATIONS
- Adept at SCRUM/Agile methodologies and environment
- Highly organized and able to work in a fast-paced environment.
- Able & willing to learn new tools, software, and processes.
- Able to take direction, listen to the needs of business managers, and interpret their needs into technical instructions.
- Effective working on teams, within, and outside of function.
- Able to work under pressure.
Develop and maintain ETL processes to ensure data quality, consistency, and accuracy. Transform and clean data as needed for downstream consumption.
Create clear and informative data visualizations and reports to communicate findings to both technical and non-technical stakeholders.
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