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The Senior Data Scientist is responsible for leveraging their expertise in statistics, computer science and business acumen to extract meaningful insights from complex datasets. This person will play a crucial role in helping organizations make data-driven decisions, solve challenging problems and drive data innovation.

The Senior Data Scientist is responsible for leveraging their expertise in statistics, computer science and business acumen to extract meaningful insights from complex datasets. This person will play a crucial role in helping organizations make data-driven decisions, solve challenging problems and drive data innovation.

Competencies:

Functional:   

• Collaborate with colleagues in other departments to improve business outcomes
• Identify and mine reliable internal and external data sources
• Design custom tools to optimize data mining, cleaning, validation and analysis tasks
• Develop and apply custom data models and algorithms to data sets
• Develop tools and testing models to ensure data accuracy
• Create and present reports that detail your findings, recommendations and solutions

Core:

• Data Acquisition and Preparation: Identifying relevant data sources, collecting, cleaning, processing, and transforming raw data into a usable format. This often involves dealing with both structured and unstructured data.
• Exploratory Data Analysis (EDA): Investigating datasets to uncover patterns, trends, relationships, and anomalies. This stage helps in formulating hypotheses and understanding the data's potential.
• Statistical Analysis and Modeling: Applying statistical methods and building mathematical models to analyze data, test hypotheses, and make predictions. This includes a strong understanding of statistical concepts, regressions, and probability.
• Machine Learning and Predictive Modeling: Developing, training, and deploying machine learning algorithms and predictive models to forecast outcomes, classify data, and automate processes. This involves selecting appropriate algorithms, feature engineering, and model evaluation.
• Data Mining and Pattern Recognition: Utilizing techniques to discover hidden patterns and insights within large datasets, which can be used for tasks like fraud detection or customer behavior prediction.
• Experimentation and Hypothesis Testing: Designing and conducting experiments (e.g., A/B testing) to validate hypotheses, measure the impact of changes, and optimize solutions.
• Data Visualization and Communication: Presenting complex findings and insights in a clear, concise, and engaging manner to both technical and non-technical stakeholders. This often involves creating reports, dashboards, and compelling visual representations of data.

Behavioral:

• Collegiality: building strong relationships on company-wide, approachable, and helpful, ability to mentor and support team growth. 
• Initiative: readiness to lead or take action to achieve goals.
• Communicative: ability to relay issues, concepts, and ideas to others easily orally and in writing.
• Member-focused: going above and beyond to make our members feel seen, valued, and appreciated.
• Detail-oriented and thorough: managing and completing details of assignments without too much oversight.
• Flexible and responsive: managing new demands, changes, and situations.
• Critical Thinking: effectively troubleshoot complex issues, problem solve and multi-task.
• Integrity & responsibility: acting with a clear sense of ownership for actions, decisions and to keep information confidential when required.
• Collaborative: ability to represent your own interests while being fair to those representing other or competing ideas in search of a workable solution for all parties.

Minimum Qualifications:

• Bachelor’s degree in computer science, Data Science, Math or related field.
• 7+ years’ experience working in healthcare
• 12+ years’ experience using Java, JavaScript, Python, R or SQL
• 10+ years’ experience in a Data Science/Analytics role
• Demonstrated expertise in statistics, computer science and business acumen to extract meaningful insights from complex datasets.
• Expertise in designing multi-cloud or hybrid data ecosystems.
• Experience with business intelligence and data analytics tools such as PowerBI
• Expertise with machine learning tech