Collects requirements and delivers high value Artificial
Intelligence (AI) / Machine Learning (ML) solutions that drive
customer and business value. Researches and builds complex, cutting
edge, and scalable AI algorithms, models, platforms, and
technologies to improve customer experience and drive business
results. Recommends changes to complex systems.
Identifies relationships and trends in data and factors that
affect the results of research.
Analyzes and interprets statistical data to identify differences
in relations among information sources.
Builds algorithms and the technologies relevant to the business
or customer experience issues.
Establishes decision strategies, using model evaluation, tuning
and performance, and operationalization and scalability of
Reports results of statistical analyses in the form of graphs,
charts, and tables.
Develops supervised and unsupervised Machine Learning (ML)
algorithms -- regression, decision trees/random forest, neural
networks, feature selection/reduction, clustering, and parameter
Evaluates and makes decisions related to new or existing tools
for various projects.
Works with large scale databases and Big Data technologies to
solve problems involving large amounts of data and computation.
Designs and develops deep neural networks and computer systems,
using ML and Deep Learning (DL) frameworks.
Deploys Artificial Intelligence and Machine Learning (AI/ML)
Prepares predictive models and develops algorithms, using
Natural Language Processing (NLP), DL, and Neural Networks
including, Reinforcement Learning.
Works on Recommender Systems, using RecSim or RecoGym,
Reinforcement Learning, Contextual Multi-Armed Bandits, Real Time
Event Detection and Scoring, Time Series Analysis, Econometrics,
and Cloud technologies.
Identifies and prioritizes AI opportunities.
Creates awareness about issues involving AI fairness evaluation,
and bias and risk mitigation for AI applications.
Creates business and technical requirements.
Transforms large volumes of data into AI-driven solutions, using
open source methods and technologies, and working with complex
business infrastructure and cross-functional teams.
Contributes to open source AI initiatives and partakes in
publications at relevant AI/ML organizations.
Leads ML strategy and road map planning.
Works across teams and influences the direction of external
Develops or applies mathematical or statistical theory and
methods to collect, organize, interpret, and summarize numerical
data to provide usable information.
Writes and delivers reports on findings for technical and
Provides mentorship and guidance for technical work of other
Translates use cases to AI/ML and analytics agenda including
data, algorithms, and validation strategy.
Drives data identification, collection, and qualification
Assists in defining data and model governance practices to
Responsible for Agile Delivery practices and project management
including, sprint planning, story creation, and backlog
Education and Experience:
Masters degree (or foreign education equivalent) in Computer
Science, Engineering, Information Technology, Information Systems,
Mathematics, Physics, or a closely related field and one (1) year
of experience in the job offered or one (1) year of experience
developing supervised and unsupervised machine learning
Skills and Knowledge:
Candidate must also possess:
Demonstrated Expertise (DE) performing advanced statistical
analytics to develop, analyze, and evaluate supervised and
unsupervised machine learning algorithms -- Regression, Decision
Trees/Random Forest, Neural Networks, Feature Selection/Reduction,
Clustering, Hyper-Parameter tuning, marketing attribution models,
and treatment control matching -- using Python, C++, Java, Spark or
scikit-learn, Tensorflow, Keras, and PyTorch, with specific use
cases on Recommender Systems and Reinforcement Learning/Bandits
applications within a planning, advice, and financial investments
DE launching Machine or Deep Learning models in online
advertising (clickstream data, Adobe, and Google analytics),
Recommender Systems (Bandit algorithms, Bayesian models, RecSym,
RecoGym, Collaborative Systems, and Reinforcement Learning), and
user behavior applications (neural network classifiers, RNNs, BERT,
Seq2Seq, LSTMs, and GANs), using Python, C++, and Java to write
production-level code to achieve greater performance; prototyping
and deploying Machine Learning solutions using experimentation
design -- design of experiments, generalized linear models, and
mixed effect models; and building service end-points, using REST
API, Flask, and Django.
DE performing data and runtime profiling, using scikit-learn,
NumPy, and pandas, cprofiler or Valgrind, SHAP or LIME, and
Facebook Prophet, Google What-If, or IBM AIF360 to detect financial
data seasonality, cycles, trends, AI fairness evaluations, and bias
mitigations; and detecting data anomalies, using machine learning
and artificial intelligence time series algorithms.
DE improving financial planning and advice offerings and
recommendations while liaising with business, product, and
engineering stakeholder teams to assess the validity of Machine
Learning models using experimentation design; and communicating
revenue or cost saving benefits to senior leadership, using
business intelligence tools -- Seaborn, Plotly, Tableau, Qlik, and
*Two positions available.
For full job details and to apply, please visit
https://jobs.fidelity.com/ and search for job number: 2014156.