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Data Scientist IV/Senior Data ScientistSenior Data Scientist

Boehringer Ingelheim
Full-time
On-site
Saint Joseph, Missouri, United States

Description

The Senior Data Scientist will execute data science projects across the company with the purpose of solving non-routine business problems by applying advanced methods including artificial intelligence, machine learning, causal inference, advanced statistics, natural language processing and other related techniques. The Senior Data Scientist is experienced in delivering successful data science projects and will work in close collaboration with all business units to develop applications which utilize data for smart decision making. Explicitly, the role will include the responsibility for designing and building computational models, discovering insights and identifying opportunities through the use of statistical and algorithmic methods (for instance machine learning) as well as data visualization techniques. Data Scientists work closely with business units and IT to turn data into critical information and knowledge. The Senior Data Scientist will utilize his/her experience to manage small and medium-sized projects, potentially including external support and to develop and mentor more junior colleagues. As an employee of Boehringer Ingelheim, you will actively contribute to the discovery, development and delivery of our products to our patients and customers. Our global presence provides opportunity for all employees to collaborate internationally, offering visibility and opportunity to directly contribute to the companies' success. We realize that our strength and competitive advantage lie with our people. We support our employees in a number of ways to foster a healthy working environment, meaningful work, mobility, networking and work-life balance. Our competitive compensation and benefit programs reflect Boehringer Ingelheim's high regard for our employees.

Duties & Responsibilities

  • Delivering successful data science projects:
    • Understand business problems and design end-to-end data science use cases
    • Collaborate across the business to understand data, IT and business constraints
    • Prioritize, scope and measure the corresponding Key Performance Indicators (KPIs)/ Objectives and Key Results (OKRs) for success
    • Collaborate with developers to implement and deploy scalable solutions
    • Establish best data operational practices and maintain all compliance requirements
    • Establish the monitoring of data science models in production
    • Lead agile approach to initiatives and launches
  • Achieving high analytical quality of delivered projects:
    • Apply strong expertise in data science to design, prototype, and build the next-generation analytics engines and services
    • Generate hypotheses about the underlying mechanics of the business process together with domain experts
    • Identify, evaluate and implement the most appropriate algorithm for the specific challenge
  • Establishing close collaborations with business units to transform towards smart decision making:
    • Guide the organization about the business potential and strategy of artificial intelligence (AI)/data science
    • Actively network on a regular basis with domain experts to better understand the business mechanics that generated the data
    • Quickly develop extensive domain knowledge in various topics
    • Train and coach other business and IT staff on basic data science principles and techniques
  • Successful management of communities and partnerships:
    • Actively network on a regular basis with internal and external partners
    • Promote collaboration and knowledge exchange with other data science teams within and outside the organization
    • Provide thought leadership by researching best practices, conducting experiments, and collaborating with industry leaders
  • Independently lead data science solution development and deployment. Deliver projects across functions and OPUs including partnerships with internal or external parties through high level of data science expertise.

Requirements

Data Scientist IV

  • Master's in data science discipline or related degree with a minimum of eight (8) years industrial experience in Data Science, Predictive Analytics, or Cognitive Analytics OR Bachelor's degree in data science discipline or related degree with a minimum of ten (10) years of industrial experience in various data science disciplines.
  • Statistics, Computer Science, Data Science certifications in a industrial quantitative performance disciplines preferred.
  • Machine/Deep Learning, CRISP-DM, and Real-time MVDA certifications preferred.

Sr. Scientist

  • Bachelor's degree in Data Science, Statistics, Computer Science or equivalent field. 

  • In addition to bachelor's degree, a minimum of seven (7) years of experience in data science, machine learning and/or MLOPs Engineering in regulated organization or similar organization. Experience must be inclusive of a minimum of three (3) years of project leadership involving program oversight for implementation of advanced analytics tools in relevant industry.

  • Expertise in SIMCA and SIMCA-online or equivalent, Seeq, JMP, R, Python, data mining and associated advanced data science tools tools, especially R and Python, AI Deep Learning, Large Language Modeling.

  • Expert in Microsoft SQL Server and relational databases.

  • Experience in Aveva PI platforms using Asset Framework and Event Frame data integrations.

  • Ability to liaison with Information Technology to ensure that the proper Extract, Transform, and Load (ETL) and database architecture is in place to fulfill MSAT needs.

  • Demonstrated expertise in systems and processes, creating and executing algorithms and distilling the solutions for a business audience, process improvement, preferably within the Pharmaceutical Industry.

  • Demonstrated understanding and ability to apply principles, concepts, practices, and standards including knowledge and use of Animal Health or Pharma data and working knowledge of industry practices.

  • Demonstrated ability to communicate ideas, facts, and technical information clearly and concisely to senior management, as well as other internal customers both verbally and written.

  • Excellent communication skills and ability to work with other disciplines.

  • Demonstrated ability to effectively manage multiple priorities.

  • Ability to lead a team or work independently with a high degree of accuracy and attention to detail in the fast-paced environment.

  • Sharp analytical abilities and proven statistics skills.

  • Models willingness to learn and stay up to date, as well as train others on data science related topics.

  • Exceptional organization and analytical skills, to critically evaluate information gathered from multiple sources, reconcile conflicts, decompose high-level information into details, abstract up from low-level information to a more general understanding, distinguish presented user requests from the underlying true needs, and distinguish solution ideas from requirements.

  • Comprehensive knowledge of database tools (Microsoft SQL Server, Aveva PI, Excel) to extract and manipulate large, complex datasets.

  • Data mining technical knowledge and skills including decision trees, multivariate analysis, segmentation modeling, factor analysis, regression analysis, forecasting, and machine learning.

  • Intellectual curiosity and commitment to teaching data analytics concepts to others in the organization or on team.

Desired Skills, Experience and Abilities

  • Advanced scientific degree desirable, e.g. PhD, MD
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