Senior Data Science Analyst
About The Position
This role guides and inspires the organization regarding the potential and strategy of data science and artificial intelligence. The Senior Data Science Analyst works with teams to understand, document, and respond to requirements for moderate to highly complex analytic needs, and identifies data-driven opportunities. This position involves understanding new data sources and process pipelines, cataloging and documenting them, and determining requirements for training and evolving deep learning models and algorithms. The role requires adherence to Data/AI Governance principles, prioritization, scoping, and management of data science projects and their key performance indicators (KPIs). The analyst will execute project tasks with urgency and high quality, communicate status clearly using departmental project management tools, follow time-tracking and project management requirements, and lead project meetings and workgroups. Additionally, the role involves preparing reports, presentations, and visualizations to share insights, collaborating with data engineers and IT teams to automate and deploy solutions, integrating performance management and data quality tools, and evangelizing organizational analytic practices. The analyst will also develop mastery in coding languages (Python, R), database programming, data wrangling, data mining, quantitative analysis, and distributed data/computing tools (MapReduce, Hadoop, HIVE). Proficiency in Enterprise Data tools and Xops methods for data pipelines is expected. The ability to communicate complex results to diverse audiences and mentor junior analysts is crucial. The role also serves as a consultant for complex statistical and analytical issues.
Requirements
- Master's degree in computer science, statistics, economics or related fields
- 5+ years of work experience in a related job discipline
Nice To Haves
- 3+ years of relevant project experience in successfully launching, planning AND executing data science projects
- Experience in other IT roles or functions such as quality assurance/testing, development, enterprise architecture, or project management.
- Demonstrated the ability to manage large data science projects and various teams
Responsibilities
- Guide and inspire the organization about the potential and strategy of data science and artificial intelligence.
- Work with teams to understand, document and respond to requirements to meet moderate to highly complex analytic needs and identify data-driven opportunities.
- Understand new data sources and process pipelines and catalog/document them.
- Determine requirements that will be used to train and evolve deep learning models and algorithms.
- Communicate and follow Data/AI Governance principles.
- Prioritize, scope, and manage data science projects and the corresponding key performance indicators (KPIs) for success.
- Execute own project tasks with urgency and to a high level of quality.
- Communicate status clearly and effectively using departmental project management tools.
- Follow time-tracking and other project management requirements.
- Lead project meetings and workgroups.
- Create datasets using Enterprise data preparation tools.
- Apply statistical analysis and visualization techniques to various data, such as hierarchical clustering, T-distributed Stochastic Neighbor Embedding (t-SNE), principal components analysis (PCA)Machine Learning to moderate to high complex questions.
- Generate hypotheses about the underlying mechanics of the business/clinical process.
- Test hypotheses using various quantitative methods.
- Proactively mine data warehouses to identify trends and patterns and generate insights for business units and senior leadership.
- Display drive and curiosity to understand the business/clinical process and collaborate with domain experts to better understand the system workflows and generation of data.
- Train other business and IT staff on basic data science principles and techniques.
- Prepare reports, presentations and visualizations to share insights with clinical, operational and research teams, using data display standards and best practice.
- Collaborate with data engineers, Xops teams and appropriate IT teams to automate and deploy solutions using established best practices,including source control and continuous monitoring.
- Integrate performance management and data quality tools into the current business infrastructure.
- Collaborate with other data science teams within the organization to evangelize and ensure compliance to organizational analytic practices and encourage reuse of artifacts.
- Collaborate with IT teams to inform Analytics production infrastructure processes.
- Develop mastery in IT skills in coding languages (Python, R, etc) and database programming languages for both relational and non relational data structures.
- Develop expertise in data wrangling, data mining, data & quantitative analysis.
- Develop proficiency in distributed data/computing tools: MapReduce, Hadoop, HIVE etc.
- Become proficient in using the Enterprise Data tools as appropriate.
- Develop strong understanding of Xops methods to partner on data pipelines.
- Develop ability to communicate complex results to technical and non-technical audiences, using story telling and creative visualizations.
- Continue to upskill through courses, local academia, networking etc.
- Ability to mentor & coach junior level data science analysts.
- Serves as a consultant to clinical, operation and research teams, regarding complex statistical & analytical issues.
Benefits
- Additional pay (e.g., shift, on‑call, or weekend differentials) and benefits may apply.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
