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KeyResponsibilities

MachineLearning and Data Modeling - Model Productionization:

-        Utilizesmachine learning (ML) and software development knowledge to implement ML modelsfor production.

-        Engagesin transforming machine learning prototypes into production-ready models.

-        Collaborateswith multiple stakeholders, such as Development Leads, Product Management,Operations, and Release Management, to make, adopt, and communicate technicaldecisions, and shape the development and delivery of software.

ModelDevelopment and Deployment - Model Deployment:

-        EnsuresML model readiness for deployment by scaling models, cleaning model code, andensuring production quality standards are met.

-        Automatesmachine learning workflows, from data extraction, transformation, and loading(ETL) to model deployment and monitoring, to establish the continuousintegration and continuous delivery of machine learning solutions.

ModelDevelopment and Deployment - Model Performance:

-        Createsinfrastructure and frameworks to monitor the performance and alignment withdesign criteria of trained models and/or systems.

-        Proactivelymonitors the performance of deployed models and troubleshoots independently orin collaboration with Data Science.

-        Developsnovel metrics that provide analytical insights to non-technical stakeholders onhow well machine learning models are operating.

ModelDevelopment and Deployment - Data Quality:

-        Evaluatespotential issues related to data quality (e.g., bias, fairness), data security,and data privacy, and minimizes their impacts on data analyses and modeling.

-        Engagesin tasks such as data cleaning, preprocessing, and feature identification toprepare for and enable model training.

InternalCollaborations and Impacts - Model Integration and Operation:

-        Collaborateswith multiple stakeholders (e.g., data scientists, software developers) tointegrate ML models into new or existing systems.

-        Maintainsthe partnership between model development and operations, ensuring smoothdeployment and continuous improvement of ML models.

-        Understandsoperational considerations of model deployment (e.g., performance, scalability,stability, maintenance).

-        Providesexpert troubleshooting and debugging support, addresses issues in machinelearning infrastructure and workflow, and creates robust solutions to preventfuture problems.

InternalCollaborations and Impacts - Tool Development:

-        Develops,maintains, and refines tools, platforms, environments, and services forinternal use.

InternalCollaborations and Impacts - Coding and Documentation:

-        Developsefficient, bug-free, medium-complexity code from scratch, and properlymaintains and organizes the existing codebase.

-        Implementsbest practices for version control, code review, and code delivery/deployment.

-        Buildsand maintains professional documentation for technical processes(experimentation, data collection and analyses, model building).

-        Testsand reviews code for bugs.

MachineLearning Expertise:

-        Maintainsfamiliarity with current developments in the machine learning field andintegrates knowledge into model development.

-        Maintainsfamiliarity with the usage and development of third-party machine learningframeworks, packages, and libraries (e.g., PyTorch, TensorFlow, Keras) tocontinuously evaluate their performance and scalability, and integrate theminto production environments.

CoreResponsibilities

Planning& Execution:

-        Managesand coordinates moderately complex tasks, monitoring timelines and deliverablesto ensure timely completion and adherence to requirements for a moderatelysized project or initiative.

-        Efficientlydelegates, monitors, and prioritizes work across multiple projects, providingtechnical oversight and adjusting plans to address shifts in resources ortimelines.

Collaboration& Partnership:

-        Collaboratesacross the organization to align on expectations and achieve shared objectives.

-        Leveragesunderstanding of business leaders, stakeholders, and/or customers to ensureproposed solutions meet their needs.

-        Supportsinclusivity by actively seeking and listening to diverse perspectives, ensuringothers feel heard and respected.

ProblemSolving:

-        Identifiesand addresses moderately complex issues by analyzing a wide range of dataand/or information to identify solutions in accordance with standard practices.

-        Proactivelyescalates unresolved or critical issues with a thorough assessment and suggestspotential solutions.

-        Reviews,contributes to, and documents problem solving strategies.

ContinuousLearning:

-        Pursueslearning opportunities to expand knowledge and skills and/or tools in new areasand stays abreast of the latest industry trends and best practices.

-        Proactivelyseeks and leverages ongoing feedback and training to improve skills.

-        Coachesand mentors junior team members, fostering continuous learning and knowledgesharing within and across teams.

ContinuousImprovement:

-        Developsideas, recommends updates, and/or collaborates on the implementation of processimprovements to increase the efficiency and effectiveness of processes,protocols, and workflows across teams, and evaluates the impact on keystakeholders.

-        Solicitsfeedback from others on ideas for alternative approaches and methods forcontinued improvement.

Performanceand Development:

-        Contributesto the talent development pipeline by participating in candidate interviews,assessing candidates, and providing hiring recommendations.