Key
Responsibilities

Machine
Learning and Data Modeling – Model Productionization:

–Utilizes
machine learning (ML) and software development knowledge to implement ML models
for production.

–Engages
in transforming machine learning prototypes into production-ready models.

–Collaborates
with multiple stakeholders, such as Development Leads, Product Management,
Operations, and Release Management, to make, adopt, and communicate technical
decisions, and shape the development and delivery of software.

Model
Development and Deployment – Model Deployment:

–Ensures
ML model readiness for deployment by scaling models, cleaning model code, and
ensuring production quality standards are met.

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

Model
Development and Deployment – Model Performance:

–Creates
infrastructure and frameworks to monitor the performance and alignment with
design criteria of trained models and/or systems.

–Proactively
monitors the performance of deployed models and troubleshoots independently or
in collaboration with Data Science.

–Develops
novel metrics that provide analytical insights to non-technical stakeholders on
how well machine learning models are operating.

Model
Development and Deployment – Data Quality:

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

–Engages
in tasks such as data cleaning, preprocessing, and feature identification to
prepare for and enable model training.

Internal
Collaborations and Impacts – Model Integration and Operation:

–Collaborates
with multiple stakeholders (e.g., data scientists, software developers) to
integrate ML models into new or existing systems.

–Maintains
the partnership between model development and operations, ensuring smooth
deployment and continuous improvement of ML models.

–Understands
operational considerations of model deployment (e.g., performance, scalability,
stability, maintenance).

–Provides
expert troubleshooting and debugging support, addresses issues in machine
learning infrastructure and workflow, and creates robust solutions to prevent
future problems.

Internal
Collaborations and Impacts – Tool Development:

–Develops,
maintains, and refines tools, platforms, environments, and services for
internal use.

Internal
Collaborations and Impacts – Coding and Documentation:

–Develops
efficient, bug-free, medium-complexity code from scratch, and properly
maintains and organizes the existing codebase.

–Implements
best practices for version control, code review, and code delivery/deployment.

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

–Tests
and reviews code for bugs.

Machine
Learning Expertise:

–Maintains
familiarity with current developments in the machine learning field and
integrates knowledge into model development.

–Maintains
familiarity with the usage and development of third-party machine learning
frameworks, packages, and libraries (e.g., PyTorch, TensorFlow, Keras) to
continuously evaluate their performance and scalability, and integrate them
into production environments.

Core
Responsibilities

Planning
& Execution:

–Manages
and coordinates moderately complex tasks, monitoring timelines and deliverables
to ensure timely completion and adherence to requirements for a moderately
sized project or initiative.

–Efficiently
delegates, monitors, and prioritizes work across multiple projects, providing
technical oversight and adjusting plans to address shifts in resources or
timelines.

Collaboration
& Partnership:

–Collaborates
across the organization to align on expectations and achieve shared objectives.

–Leverages
understanding of business leaders, stakeholders, and/or customers to ensure
proposed solutions meet their needs.

–Supports
inclusivity by actively seeking and listening to diverse perspectives, ensuring
others feel heard and respected.

Problem
Solving:

–Identifies
and addresses moderately complex issues by analyzing a wide range of data
and/or information to identify solutions in accordance with standard practices.

–Proactively
escalates unresolved or critical issues with a thorough assessment and suggests
potential solutions.

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

Continuous
Learning:

–Pursues
learning opportunities to expand knowledge and skills and/or tools in new areas
and stays abreast of the latest industry trends and best practices.

–Proactively
seeks and leverages ongoing feedback and training to improve skills.

–Coaches
and mentors junior team members, fostering continuous learning and knowledge
sharing within and across teams.

Continuous
Improvement:

–Develops
ideas, recommends updates, and/or collaborates on the implementation of process
improvements to increase the efficiency and effectiveness of processes,
protocols, and workflows across teams, and evaluates the impact on key
stakeholders.

–Solicits
feedback from others on ideas for alternative approaches and methods for
continued improvement.

Performance
and Development:

–Contributes
to the talent development pipeline by participating in candidate interviews,
assessing candidates, and providing hiring recommendations.