Location: 100% REMOTE
Duration: 6 months
Interviews: MS Teams video
Pay Rate Range: $75/hour
The Division of Information Technology Services (ITS) is currently seeking a talented individual to fill the role of Machine Learning Engineer. We are looking for an expert in machine learning to help us extract value from our data. The person who fills this role will lead all the processes for new virtual assistants, from data collection, cleaning, and preprocessing, to training models and deploying them to production. This individual will be responsible for aligning the models with the university’s overall machine learning strategy and road map.
The ideal candidate will be passionate about artificial intelligence and stay up-to-date with the latest developments in the field. This individual will also be passionate about delivering an exceptional customer experience. We are currently in development of 2 bots hosted on the ServiceNow and the Microsoft Azure Bot Service platforms. This role will be responsible for updates and maintenance, and ongoing customer support as necessary.
We are looking for a mentor to work with resources within and outside of existing Customer Experience (CX) team to leverage our data and provide an enhanced experience for our community. This role will need to collaborate and work with key groups and stakeholders across IT Services and the university. Representing ITS, the successful candidate will have strong collaboration, communication, and customer service skills to successfully partner with members across the community (at various levels).
To ensure that essential services are provided to the university community, a flexible schedule is required. The employee may be required to work outside his/her regular working hours and on university holidays.
Excellent interpersonal, communication and organizational skills are required. Excellent analytical and troubleshooting skills are necessary.
Strong written communication skills are preferred, as is the ability to adapt to and follow an organization’s voice, tone, and style.
Breakdown of time:
Analysis- 50% of the time
Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
Verifying data quality, and/or ensuring it via data cleaning
Supervising the data acquisition process if more data is needed
Finding available datasets online that could be used for training
Design – 25% of the time
Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
Defining validation strategies
Defining the preprocessing or feature engineering to be done on a given dataset
Defining data augmentation pipelines
Deploy – 15% of the time
Managing available resources such as hardware, data, and personnel so that deadlines are met
Training models and tuning their hyperparameters
Analyzing the errors of the model and designing strategies to overcome them
Deploying models to production
Collaboration – 10% of the time
Collaborating with customers, stakeholders, and service owners to build out solutions