Senior Manager, Machine Learning Engineering

KeyResponsibilities

MachineLearning and Data Modeling Model Productionization:

Facilitatesthe implementation of machine learning (ML) model productionization processesand process improvements.

Usestechnical knowledge and business familiarity to empower the transformation ofmachine learning prototypes into production-ready models.

Implementsstrategy to build technical expertise and readiness across team related tomodel productionization.

Alleviatesthe impact of obstacles on collaboration with multiple stakeholders such asDevelopment Leads Product Management Operations and Release Management tomake adopt and communicate technical decisions and shape the development anddelivery of software.

ModelDevelopment and Deployment Model Deployment:

Implementsmultiple team standards around ML model readiness for deployment (e.g. modelscaling model code cleaning and meeting production quality standards).

Promotesmultiple team strategy around the automation of machine learning workflowsfrom data extraction transformation and loading (ETL) to model deployment andmonitoring to establish the continuous integration and continuous delivery ofmachine learning solutions.

ModelDevelopment and Deployment Model Performance:

Promotesmultiple team strategies around trained model/system alignment with designcriteria.

Identifiesimprovements within multiple team processes around deployed model performanceevaluation and troubleshooting.

Facilitatesthe creation of novel metrics that provide analytical insights to non-technicalstakeholders into how well machine learning models are operating.

ModelDevelopment and Deployment Data Quality:

Implementsimprovements to multiple team processes for the identification and evaluationof potential issues related to data quality (e.g. bias fairness) datasecurity and data privacy and the minimization of their impacts on dataanalyses and modeling.

Promotesmultiple team strategies for preparing for and enabling model training.

InternalCollaborations and Impacts Model Integration and Operation:

Implementsimprovements to multiple team strategy that forms partnerships forcollaboration with multiple stakeholders (e.g. data scientists softwaredevelopers) to integrate ML models into new or existing systems.

Maintainsaccountability of model development and operations teams in the smoothdeployment and continuous improvement of ML models.

Buildsthe teams knowledge of operational considerations of model deployment ( scalability stability maintenance) to facilitate multiple teamprocesses.

Facilitatesexpert troubleshooting and debugging support efforts to address issues inmachine learning infrastructure and workflow and create robust solutions toprevent future problems.

InternalCollaborations and Impacts Tool Development:

Implementsprocess improvements for the development refinement and maintenance of toolsplatforms environments and services for internal use.

InternalCollaborations and Impacts Coding and Documentation:

Implementsimprovements to multiple team processes for the development of efficientbug-free code from scratch as well as the maintenance and organization of theexisting codebase.

Maintainsteam adherence to best practices for version control code review and codedelivery/deployment.

Monitorsprofessional documentation for technical processes (experimentation datacollection and analyses model building).

MachineLearning Expertise:

Implementsmultiple team strategies to maintain team awareness of current developments inthe machine learning field and integration of this knowledge into modeldevelopment.

Utilizesfamiliarity with the usage and development of third-party machine learningframeworks packages and libraries (e.g. PyTorch TensorFlow Keras) toidentify improvements to multiple team processes around their performance andscalability and integrate them into production environments.

CoreResponsibilities

Planning& Execution:

Managesmultiple medium- to large-scale projects or initiatives across teams ensuringtimelines deliverables and budgets when applicable are monitored and met.

Providesdirection to teams on project work setting priorities and aligning withbusiness needs.

Guidesteams on adjusting plans to accommodate resource or timeline changes.

Collaboration& Partnership:

Drivescross-functional partnerships to align expectations and shared objectivesacross multiple teams.

Coachesteam members to develop strategic relationships with business leadersstakeholders and external partners to foster collaboration and long-termsuccess.

Promotesinclusivity by actively seeking and listening to diverse perspectives ensuringothers feel heard and respected.

ProblemSolving:

Providesdirection to multiple teams on addressing complex operational and/or technicalissues as well as providing guidance on analyzing complex data and/orinformation to identify solutions.

Reviewsand provides insights into unresolved or critical issues helping the team toidentify potential solutions.

ContinuousLearning:

Modelsengaging in continuous learning to deepen expertise and stay ahead of industrytrends integrating best practices into strategic planning.

Leveragesfeedback to drive personal and team skill improvements.

Identifiesskill gaps across teams and empowers team members to pursue learning andknowledge sharing opportunities that build their expertise in new areas andcoaches them to apply learnings to advance the organization.

ContinuousImprovement:

Drivesteam to collaborate on develop and implement ideas to increase the efficiencyand effectiveness of processes protocols and workflows within and acrossteams providing oversight.

Guidesteam to adopt new ideas for alternative approaches and methods and encouragesfeedback for continued improvement.

Performanceand Development:

Drivesperformance across teams by providing feedback and coaching in alignment withperformance management processes guidelines and expectations.

Discussesdevelopment goals with team members shares opportunities to facilitate careerdevelopment and ensures individual goals are aligned with broaderorganizational goals.

Developsand manages talent acquisition pipeline by leading candidate interviewsmonitoring promotion eligibility and/or orchestrating talent resources.