Manager, Machine Learning & Data Engineering

Key Skills

AWS, Azure, D3.js, Data Analysis, Data Engineering, DevOps, Google Cloud Platform (GCP), Machine learning techniques, MLOps tools, Natural Language Processing (NLP), NoSQL, PowerBI, Python Programming, R Programming, SPARK Programming, SQL, Tableau

Job Description

This role will collaborate with our business teams, data scientists, engineers and operational support to understand the business requirements that drive Omnichannel, Sales operations and Marketing solutions They will be involved in the machine learning model development, full feature engineering roll out and support with responsibilities for designing, configuring, implementing, and supporting leading-edge data pipelines that support key decisions and drive organizational action for our Omnichannel brands. This will include interpreting our business use cases and mapping them to creating new algorithms and enhancing our existing models working with our advance analytics teams (business translators), data engineers, data stewards, sales optimization and marketing teams. The role will also include close partnership with our external ML Operations and Dev Operations partner, including management and monitoring their effective delivery of JET-related work.

As the primary lead for a designated therapeutic area (e.g., Oncology), they will drive the forward-looking vision for Janssen’s data sciences and engineering strategy, leading the deployment and support of Janssen’s leading-edge models and pipelines to advance Commercial Strategy and Optimization data sciences and enabling the data engineering capabilities. This role will act as the primary contact for the Data Sciences & Engineering Omnichannel workstream across the specified therapeutic area for CS&O, internal IT, and services/software partners, providing regular communications supporting Omnichannel vision, strategy, and operations.

This role will include supervisory and individual contribution components (i.e., a player-coach). Leadership acumen as well as senior-level technology capabilities are required. It is important that this person has strong technical and business capability in equal measure, including senior-level written and verbal communication skills across both audiences.

Role & Responsibilities:

Scale up, Enhance and Support the Omnichannel machine learning & data engineering track for the Commercial Strategy and Operations group. Assist senior leadership team in evolving and executing the data sciences & engineering strategy.

• Understand Janssen commercial business unit’s Omnichannel data and functional requirements to scale up and support feature engineering for our therapeutic areas.
• Act as a subject matter expert in data sciences & engineering frameworks and bring best in class, innovative ideas to develop, test, and measure performance and impact of initiatives
• Work in collaboration with business translators (advanced analytics), other data scientists and engineers to present the findings of the machine model runs and data insights to our key stakeholders (brands and leadership)
• Understand existing data science models and engineering pipelines related to Johnson and Johnson Innovative Medicine’s omnichannel brands, provide inputs and suggestions to optimize the architecture, provide recommendations on performance improvement
• Collaborate with omnichannel operations support lead & partners to leverage DevOps capabilities to operationalize models & data engineering Pipelines.
• Make recommendations on value and efficiency of new capabilities and technologies in order to advance adoption of data sciences and engineering capabilities across innovative medicine.
• Act as a change agent for making machine learning and advance analytics capabilities more pervasive across the organization by creating proof of concepts & demo capabilities working with internal teams and external partners

Drive the design and build of new data pipelines & feature engineering layer

• Apply explainable artificial intelligence (XAI), data engineering and feature engineering principles to support data science requirements and supply raw, curated, and processed data for machine learning engineers and data scientists
• Work in cross-functional agile teams to continuously experiment, iterate, and deliver business goals and objectives
• Collaborate with other data engineers, machine learning staff, and stakeholders from multiple therapeutic areas to take learnings and synergies as they arise
• Lead the development and implementation of data quality framework for feature engineering working with upstream data systems
• Proactively identify new data sources that will enhance decision making & increase model accuracy

Innovation and Early Talent Development

• Work on rapid prototyping applying innovative data sciences and engineering capabilities to address sales & marketing business use cases
• Provide thought leadership by researching best practices, conducting experiments, and collaborating with industry leaders
• Lead our early talent development program working with internal team members and university across US region to attract and hire talent in for the Janssen Commercial Unit

QUALIFICATIONS

Education

• Minimum of a Bachelor degree with 7 + years of experience required in Computer Science, Mathematics, Statistics or Computational Science. MS and 5+ years of experience or PhD and 2+ years of work experience preferred.
• 1 Year of experience managing a team.
• Strong working knowledge of machine learning algorithms, including one or more machine learning techniques such as regression, decision trees, probability networks, association rules, clustering, neural networks, and/or Bayesian models.
• Strong working knowledge of machine learning platforms/environments (python, supervised and unsupervised machine learning, AWS).
• Experience delivering on data science projects using predictive technologies, data mining, and/or text mining, natural language processing (NLP).
• Experience with SQL and/or NoSQL.
• Experience with exploratory data analysis, data transformation, feature creation & engineering.
• Strong business and data analysis skills with experience in life sciences, health care or pharmaceutical commercial operations, or equivalent.
• Experience with building and supporting container-based pipelines using cloud technologies such as AWS, Google, or Azure.
• Strong understanding of data lifecycle, data quality, metadata and data management.
• Ability to work in an agile manner, learn and apply new technologies, and be a key contributor as well as manage projects independently.
• Strong verbal and written communication skills and a proven ability earn business stakeholder trust.
• Proven analytical and problem-solving abilities.
• Familiarity with Machine Learning Operations (ML Ops) and Data Science Development Operations (Dev Ops for Data Science).
• Strong knowledge of one or more of R/Python/SPARK/EMR/Glue/RedShift.
• Knowledge of data querying/data analysis BI tools (Tableau, PowerBI, SQL workbench).
• Knowledge of data management related standard operating procedures.
• Knowledge of data governance best practices.

Preferred Skills:

• Demonstrated skills in team and culture building, focused on retaining and mentoring emerging talent.
• Presentation skills in front of small/medium audiences.
• Knowledge of commercial life sciences data sets (NPP, DDD, Xponent, Plantrak, SPP).
• Knowledge of commercial and channel life sciences data sets (LAAD, Speaker Program, Channel – Email, Non-Promotional Personal, CRM, Social Media, Doximity).
• Experience with natural language processing (NLP).
• Knowledge of large language models (e.g., BARD, OpenAI).
• Experience with scientific visualization techniques (RShiny, Tableau, Observable, PowerBI, d3.js).

Other Details:

• This position requires up to 10% domestic travel
• The anticipated base pay range for this position is: $113K to $170K USD