Bank of America Data Scientist - Charlotte, NC in Charlotte, North Carolina

Job Description:

Responsible for enabling analysis, modeling, and optimization through producing information products. Involved in the research and development efforts. Primary requirement is not related to traditional programming or systems analysis skills, but to the ability to support the creation of sophisticated, value-added analytic systems that support revenue generation, risk management, operational efficiency, regulatory compliance, portfolio management, and research. These systems must overcome issues of complex data (e.g., VLDB, multi-structured, "big data", etc.) as well as deployment of advanced techniques (e.g., machine learning, text mining, statistical analysis, etc.) to deliver insights. This role often possesses a degree in hard science or another heavy quantitative business or social discipline. Able to work independently or in a team on complex projects.

The Chief Data Scientist organization facilitates information advantage and a unified data science strategy across Bank of America.

Role Overview:

We are looking for Data Scientists who will help us discover the information advantage hidden in vast amounts of data, and help us make smarter decisions to deliver even better products and services. Your primary focus will be managing the intake of AI (Artificial Intelligence) solutions which accelerates the development of advanced analytics solutions throughout the enterprise. Your expertise will connect teams to tools, platforms, and subject matter experts (SMEs) to apply data science techniques, including machine learning, deep learning, natural language processing, chat bots, and virtual assistants. Additionally, the role will serve to guide and ensure the governance of solutions going through the AI Intake process.

Key Responsibilities:

  • This role will work with Line of Business and Control Function partners to accelerate their data science solutions

  • Individual contributor role enabling data science at Bank of America

  • Take a leadership role in the data science community of experts

  • Analyze and ensure documentation of AI solutions while aligning project teams to governance areas such as Data, Vendor, EARC, and Model Risk Management

  • Connect project teams to resources including lab environments, communities of experts, and SMEs in the Chief Data Scientist organization

  • Communicate findings, gaps and opportunities to all levels of the organization

  • Limited time spent delivering data science solutions:

  • Building and optimizing classifiers using machine learning techniques

  • Doing ad-hoc analysis and presenting results in a clear manner

  • Extending company’s data with third party sources of information when needed

  • Processing, cleansing, and verifying the integrity of data used for analysis

  • Feature engineering

  • Data visualization

This position manages the inventory and AI Intake process which enables the development of responsible AI for the enterprise. The goal is to move from individually managed governance processes that are inconsistently engaged to an AI risk framework that contemplates, organizes and validates appropriate engagement of governance processes. Additionally, the role will identify opportunities for improving and accelerating the development of AI during the Intake process for initiatives.

The accepted definition of AI within Bank of America is algorithms and models built using advanced statistical techniques such as machine learning, neural networks, deep learning, or genetic algorithms. In concrete technical terms, these are models or algorithms where regularization is achieved through sampling. These tools can be utilized for improved predictions, enhanced decision making and/or to replicate human cognition. They can be self-learning tools that evolve over time in a semi-automated or automated fashion.

AI includes language processing and text mining algorithms, image recognition or classification algorithms, models built with boosted trees or random forests, models built with convolutional or recurrent neural networks, models or algorithms with automated reprogramming of predictions based on new information

AI DOES NOT include robotic process automation, applications with purely deterministic logic (i.e., no probabilities or estimation involved), ordinary Least-Squares (“OLS”), logistic regression, or time series models

Primary Functions:

The role will perform 3 major functions:

  • Manage the AI Intake process which serves as the funnel for driving teams to data science resources and governance requirements in their AI initiatives.

  • This position will manage the build-out and enhancements of the AI inventory (projected to be developed in Diamond).

  • The role will rely on a technical understanding of varying solutions to connect to enablement resources, identify risks, and ensure the appropriate governance is achieved.

  • Develop routines and scanning processes to capture AI development efforts and initiatives at the first stages in their lifecycle.

  • This includes connecting with leadership within each CIO area to document priorities and scanning methods to identify potential AI development across the enterprise (i.e. licenses for R, Python, and DataRobot).

  • Co-Lead the AI and Data Science Community of Experts.

  • This community enables data science collaboration and drives the enterprise to best practices for tools, data sourcing/permissible use processes, and governance guidance.

  • The position will run forums, sessions and events with enterprise experts in data science that provide a venue for teams to showcase their successes in AI and data science while providing a forum for teams to collaborate and accelerate their AI development.

Role Qualifications:

Find relationships in hyperspace

Classification (e.g. gradient boosting methods, random forest, bootstrap aggregation, decision tree, stumps, shrinkage, number of trees, basis expansion functions, exponential loss function, gradient descent optimization [stage-wise versus global], support vector machine, convolutional neural net, recurrent neural net, recursive neural net, etc.)

Regression (e.g. gradient boosted regression tree, lasso, ridge, loess, spline, K-nearest neighbor, kernel methods, infinite basis representations with neural networks, symbolic regression with genetic algorithms, representation learning etc.)

Similarity matching with known attributes (e.g. affinity propagation, manifold learning, multi-dimensional scaling, kernel principal component analysis, feed-forward deep belief networks, convolutional neural networks, recurrent neural networks, recursive neural networks, reinforcement)

Finding relationships in one-dimensional time

Autoregressive Integrated Moving Average (ARIMA), Christiano-Fitzgerald filter, high-low bandpass filters, Granger causation, hidden Markov model (HMM) for adding hidden states, Softmax autoencoder for regime change, support vector machine for noise reduction, etc.

Finding relationships in text

Word2Vec, Glove, recurrent neural networks (RNN) long-short term memory (LSTM), recursive neural networks for parse trees, dialogue management, intent management

Finding relationships in entangled space – graphs

Link analysis, degree centrality, eigenvalue centrality, betweenness centrality, closeness centrality, PageRank centrality, deep queries with various levels of indirection, etc.

Reducing dimensionality of hyperspace

Principal component analysis (PCA), Independent component analysis (ICA), autoencoders, K-means, K-medoid, BIRCH, spectral clustering (eigenvalues), agglomerative hierarchical clustering (Ward’s), etc.

Determining causation from correlation and validation

Confusion matrix, recall, precision, accuracy, bias versus variance trade-off, degrees of freedom, leakage, effect from missing values, etc.

Required Skills:

  • Strong STEM background - practical experiences in Machine Learning, NLP, Deep Learning (Tensorflow), Statistical Modeling, Quantitative Analysis, Forecasting, Data Visualization

  • Understanding of machine learning techniques and algorithms, such as Gradient Boosted Trees, CNN, RNN LSTM, etc.

  • Knowledge and moderate proficiency in R, Python (scikit learn, pandas, numpy, Jupyter etc.) SAS, SQL, MATLAB, C/C++ Experience with data visualization tools ex Tableau, Shiny

  • Excellent communication and presentation skills

  • Highly organized and experienced in leading/managing complex projects, initiatives and development

Posting Date : 07/09/2018

Location :


  • United States

Travel : No

Full / Part-time : Full time

Hours Per Week : 40

Shift : 1st shift

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