Senior Data Scientist
Section 1: Position Summary
The Senior Data Scientist will identify advanced analytic opportunities independently and effectively applying data mining and math principles capitalizing on available and new technologies to deliver predictive models, analytics insights, simulations and optimizations that drive measurable value.
Section 2: Job Functions, Essential Duties and Responsibilities
- Master technologies and data skills
- Execute on advanced knowledge of analytics technologies to optimize results. Identify technology gaps and recommend solutions.
- Display advanced knowledge of SQL and data models. Write SQL to optimize streams and support data modeling discussions.
- Solve integration and performance challenges developing skills as needed and engaging others in solutions
- Drive benefit thru influence and communication of advanced analytics results
- Support data driven decisions recommending corrective action and improvements
- Apply business and predictive modeling expertise to identify underlying trends
- Prepare and deliver presentations detailing results and helping others understand analytics. Align presentations effectively to the audience.
- Identify and recommend improvements to models, analytics, reporting and research
- Manage and oversee consistent, controlled and quality development, versioning, storage and deployment of analytics effectively integrating with applications and data sources
- Establish model schedules or invoke real time through workflow and other application integration
- Solve integration challenges
- Be an effective mentor and leader
- Support quality and meaningful results thru training and review other’s analytics as well as ensuring personal deliverables are validated
- Manage own and assigned team work output; delivering on priorities; setting and managing to delivery commitments; storing results
- Follow and ensure others in team follow EIM data & delivery governance
- Mentor and develop analytics team
- Perform complex data analyses, segmentation and profiling of expansive data sources
- Develop and maintain models effectively engaging business and data experts following established process and governance.
- Validate that data sources meet requirements of the effort and outcomes
- Analyze data quality identifying improvements and solutions for data gaps.
- Develop extensive working knowledge of Ascensus data sources purpose, structure and definitions
- Regularly use complex data and algorithms
- Effectively execute CRISP-DM methodology directing project teams to capitalize on team expertise and optimize results including predictive model lift
- Responsible for protecting, securing, and proper handling of all confidential data held by Ascensus to ensure against unauthorized access, improper transmission, and/or unapproved disclosure of information that could result in harm to Ascensus or our clients.
- Our I-Client service philosophy and our Core Values of People Matter, Quality First and Integrity Always® should be visible in your actions on a day to day basis showing your support of our organizational culture.
- Assist with other tasks and projects as assigned
Section 3: Experience, Skills, Knowledge Requirements
- Advanced analytics: the ability to apply appropriate analytical techniques and translate findings into actionable business insights and recommendations. Identify and utilize best math principles and methods to achieve goals tapping into experience, knowledge and research
- Database technologies: understanding of data warehousing concepts including displayed experience or knowledge of data mart and data definition practices that support quality and re-use, with the ability to write complex SQL statements to store, retrieve, manipulate, integrate, validate, and summarize data
- Strong communications skills: both verbal and written, with the ability to explain complex concepts to non-technical audiences
- A team orientation: strong interpersonal and collaboration skills, with the ability to network with internal business partners across the organization
- Graduate degree in a field specifically aligned with data analytics. e.g. artificial intelligence, statistics or other
- Bachelor’s degree in a quantitative or technical field, such as computer science, mathematics, engineering, or economics
- Solid business acumen with the ability to quickly learn about Ascensus products, services, clients and business considerations. Ability to obtain valuable information and insight through dialog with SMEs.
- Time Management/Prioritization – Ability to manage time and lead others to prioritize work to meet deadlines and deliver most valuable results first. Responsibility to manage own and others tasks as assigned along with resolving priority conflicts and collaborating to ensure common expectation understanding with clients.
- Initiative/Work Ethic – Drives efforts to completion setting and managing personal goals. Proactively seeks out opportunities to ensure successful outcomes.
- 6-8 years of experience displaying post academic understanding of how to utilize math principles and technologies to benefit business objectives
- A wide spectrum of database and statistic technology experiences demonstrating ability to learn new technologies and advise on best solutions
- Modeling Experience with any of the following; NLP (Natural Language Processing), Classification algorithms, SVM (Support Vector Machines), Bayesian models, Logistic Models, ANN (Artificial Neural Networks), classification and clustering algorithms, and naïve bayes.
- Experience with the following software; R, Python, SPSS, Cognos
We are proud to be an Equal Opportunity Employer
- Collaborative, idea-sharing environment
- Professional development
- In-house training
- Tuition reimbursement
- Generous reward programs
- Paid time off (additional purchase plan)
- Medical, dental & vision benefits
- Health savings account (employer contribution up to $1,100)
- 401(k) & 529 match programs
- Volunteer/charitable-giving programs
- Business casual dress