Senior Data Scientist leveraging advanced analytics to drive supply chain solutions at Johnson & Johnson. Collaborating with teams to develop predictive models and enhance decision-making in healthcare.
Responsibilities
Gather business requirements from business partners and accurately translate those into digital & data science solution prototypes involving predictive analytics/simulation components, validate the prototypes and work closely with product development teams to scale the prototypes
Build predictive/prescriptive model prototypes encoding complex business processes for deployment of solutions supporting business KPI improvement
Work closely with product development teams to ensure that prototypes are scaled optimally with respect to adherence to business requirements, solution cost and agility to flex the solutions
Work closely with vendors to deploy third party algorithmic components into our digital platforms.
Assist digital product managers in new digital product launches i.e. ensuring that the right user tests are designed for product validation, develop metrics and product analytics to monitor and optimize products
Assist digital product managers in sustaining and optimizing existing digital & data science products and platforms. This would involve continuous improvements to already deployed models by testing new generations of data science models, evaluating optimal ways to implement those models (e.g. open source vs vendor sourced analytic components)
Monitor external trends on new types of modeling approaches & solution capabilities to continuously improve deployed digital solutions in targeted supply chain areas such as value chain management, planning, customer analytics, supplier risk management etc.
Requirements
Masters degree in Operations Research/Econometrics/Financial Engineering/Systems Engineering or other STEM disciplines.
At a minimum, 2-3 years of work experience is required.
Must have demonstrated competence in working with business partners to translate business requirements into digital solution features and data science models
Strong expertise in Python libraries such as numpy, scipy, pandas, scikit-learn, pyomo, simpy etc..
Proven experience demonstrating deployment of models built using python libraries in production environment
Must have expertise in SQL, querying databases for exploratory data analysis and data profiling.
Some degree of experience with NOSQL databases such as Graph Databases (E.g. NEO4J) is preferred
Must have experience with deployment of open source based models into production within cloud based digital stacks.
Candidates must have demonstrated the ability to work with data engineers to leverage data pipelines for operationalizing models.
Must have expertise in various types of Operations Research models E.g. Optimization models (Linear Programming, Non-Linear Programming, Mixed Integer Programming), Simulation models (Discrete Event Models, Monte Carlo Simulations Etc.), parameter estimation models (e.g. Markov Chain Monte Carlo Etc.)
Experience with modeling supply chain management particularly in the healthcare industry (pharmaceuticals/medical devices) is highly desired
Familiarity with Azure or AWS Cloud components such as Databricks and associated big data frameworks such as Spark, PySpark etc.
Must have intermediate to high degree of expertise in tableau or similar visualization tools to created prototypes of dashboards for validation and insights delivery
Familiarity and experience with ERP systems like SAP including unstructured data sources is preferred
Benefits
Vacation –120 hours per calendar year
Sick time - 40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
Holiday pay, including Floating Holidays –13 days per calendar year
Work, Personal and Family Time - up to 40 hours per calendar year
Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
Caregiver Leave – 80 hours in a 52-week rolling period10 days
Volunteer Leave – 32 hours per calendar year
Military Spouse Time-Off – 80 hours per calendar year
Senior Health Data Scientist leading complex data extraction and modeling for healthcare solutions at Inovalon. Collaborating with multidisciplinary teams to deliver data - driven insights.
Data Scientist developing machine learning solutions and delivering insights for operational decisions. Collaborating with stakeholders to apply analytical techniques and improve business outcomes.
Data Scientist responsible for modeling and analyzing credit risk at CAIXA Consórcio. Utilizing data - driven insights to support strategic decision - making in credit operations.
Data Scientist optimizing payments ecosystem for Preply, enhancing user experience through data - driven insights. Collaborating with teams to improve payment processes and fraud management.
Staff Data Scientist at Preply developing data strategies for product domains. Collaborating with executives to drive long - term strategy and experimentation frameworks.
Data Manager leading data strategy and governance for Global Payments Solutions at Bank of America. Managing data architecture aligning with business and regulatory needs while overseeing complex data ecosystems.
Data Scientist developing and implementing LLM - based agents and leveraging AI techniques to improve client value. Collaborating on project challenges in a dynamic, start - up environment at Gartner.
Data Scientist in AI SaaS integrating 100+ systems for a European unicorn - in - the - making. Ensure scalability, security, and performance in a high - growth environment.
Data Science Intern working on AI - driven recipe and hardware optimization problems in semiconductor processes. Developing machine learning models and collaborating with engineering teams for innovative solutions.
Senior Data Scientist at LexisNexis developing AI - driven solutions for legal analytics. Collaborating with teams to implement machine learning models and monitor performance metrics.