Data Scientist

Responsibilities

• Collaborate with data engineers and stakeholders to design, refine, and deploy data pipelines that feed models and analytics processes.
• Create and maintain dashboards, visualizations, and reports that communicate complex analyses in a clear and actionable manner using tools like Tableau or other BI platforms.
• Perform exploratory data analysis (EDA) to identify trends, patterns, and insights, and communicate findings to stakeholders.
• Develop and implement models, including statistical and machine learning models, to support business decision-making.
• Develop and maintain data processing workflows for model training, evaluation, and deployment.
• Work closely with cross-functional teams to understand their data needs and translate business problems into analytical questions.
• Develop and maintain documentation for data science workflows, models, and methodologies that help achieve business goals and support impact measurement.

Skillset

• 2 to 5 years of relevant professional experience in data science, machine learning, or a related field.
• Strong foundation in statistics, data analysis, and machine learning algorithms.
• Proficient in Python or R, with experience in using libraries such as pandas, sci-kit-learn, TensorFlow, or PyTorch.
• Experience working with cloud-based platforms such as Google Cloud, AWS, or Azure, especially in deploying machine learning models and querying data from data warehouses.
• Substantial experience in SQL and working with large datasets, including data wrangling, cleaning, and transformation.
• Familiarity with data engineering tools and concepts, and a strong understanding of how data models and pipelines support advanced analytics.
• Excellent communication skills, with the ability to present complex analytical concepts to non-technical audiences in an understandable and engaging manner.
• Experience with best practices for tools like Jupyter Notebooks, Git, and version control to ensure robust, reproducible analyses.
• Proven ability to take projects from conceptualization to deployment, with an aptitude for problem-solving and troubleshooting.