Senior Data Scientist

Jersey City, NJ, USA | Fidelity Investments

  • Industry:
    Financial Services
  • Position Type:
  • Functions:
    IT / Information Technology
  • Experience:
    3-5 years
Job Description:
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3+ years of experience crafting, developing, validating, and deploying predictive models that directly support business decision-making

Advanced degree in Engineering, Computer Science, Computational Statistics, Operations Research or equivalent experience

Proven experience with advanced Machine Learning techniques including neural networks, deep learning, reinforcement learning, support vector machines, principal component analysis, regression, time series analysis and clustering

Background in projects involving large scale-multi dimensional databases, complex business infrastructure, and multi-functional teams

The Skills You Bring

Proven experience using mainstream languages, such as Python, R, or equivalent to source, cleanse, and model large data sets.

Skilled in visualizing and communicating data using packages such as d3.js, Plotly, and Seaborn, and tools such as Tableau and Qlik

Hands-on experience applying machine learning techniques using the standard data science tool kits and libraries

Intermediate level experience with sourcing and wrangling data from warehouses, Big Data (e.g. Hadoop, Spark) and other sources using SQL and scripting

Comfortable with working across technologies and business functions, as well as the ability and willingness to develop solutions at all levels of the data science stack, from data engineering and sourcing to productionalization.

Self-starter mindset – the ability to manage ambiguity, and identify what needs to be done and execute rather than waiting for explicit direction

The Value You Deliver

Discovering and verifying new opportunities to identify risk, grow business, scale and optimize operations

Helping Senior Leaders to make decisions utilizing the data and analytical skills

Helping to structure work, planning new analyses, translating business questions into analytical projects

Identifying and ingesting new data sources and performing feature engineering for integration into models