Carries advanced analytics and artificial intelligence (AI) use
cases from concept through client-facing implementation. Translates
business needs into quantitative use cases for research and
Translates qualitative business-level requests into technical
Engages with architects and data engineers, governance
stakeholders, and scientists to develop models, prove their
effectiveness, and demonstrate results.
Responsible for simultaneous use cases at different stages of
maturity in their lifecycles and effectively switches contexts on
demand as required.
Applies machine learning to data to develop analytics and
Assists in developing data strategies.
Serves as an analytics subject matter expert within the
Educates team members and associates within the organization
about Machine Learning (ML) and AI.
Education and Experience:
Bachelors degree (or foreign education equivalent) in Computer
Science, Engineering, Information Technology, Information Systems,
Mathematics, Physics, or a closely related field and six (6) years
of experience in the job offered or six (6) years of experience
developing and launching machine learning algorithms in the wealth
and relationship management, marketing, and sales industries for
client-facing financial platforms.
Or, alternatively, a Masters degree (or foreign education
equivalent) in Computer Science, Engineering, Information
Technology, Information Systems, Mathematics, Physics, or a closely
related field and four (4) years of experience in the job offered
or four (4) years of experience developing and launching machine
learning algorithms in the wealth and relationship management,
marketing, and sales industries for client-facing financial
Skills and Knowledge:
Candidate must also possess:
Demonstrated Expertise (DE) developing supervised, unsupervised,
and reinforcement machine learning algorithms (regression, decision
trees/random forests, neural networks, feature selection/reduction,
clustering, and parameter tuning), using programming languages
(Python, PySpark, and Scala) and tools (Tensorflow, AWS services,
d3.js, Gephi, and R).
DE designing and developing data pipelines, cleansing data, and
constructing feature engineering routines, using Hive, PySpark, and
Pandas; performing natural language processing, using Gensim, NLTK,
and Spacy to create data sets used for statistical models and
machine learning; and creating data sets used for statistical
models, using advanced transformation techniques (SQL, Hive, or
Impala) and Big Data.
DE executing data science projects across computing environments
and platforms (Linux, Windows, Oracle/SQL, Greenplum/Postgres, and
Hadoop/Hive); performing software development -- programming in
Python and using data science libraries (NLTK, SciPy, Scikit-learn,
NumPy, or Pandas) to create feature engineering pipelines or build
machine learning models -- in a large financial services
DE applying software development best practices -- source code
management, documentation, code reviews, frameworks, and
dependencies -- to create code during the course of research, using
Jupyter Notebook, PyCharm, Bitbucket, and Git-Stash; coordinating
with stakeholders to assign data engineering and analysis tasks,
using project methodologies (Agile, Scrum, or Kanban) and
technologies (Atlassian Stack and Trello); communicating with
business stakeholders using Edifier, Skype, and Microsoft Teams to
gather and interpret business and stakeholder requirements for data
science projects; and writing stories and acceptance criteria,
setting story priorities, and tracking requirement fulfillment
progressing using Jira.
For full job details and to apply, please visit
https://jobs.fidelity.com and search for job number: 2006128