Machine Learning Engineer in Irving, TX

TSR Consulting is seeking a Machine Learning Engineer for a financial services client in Irving, TX

Summary

Act as Machine Learning Engineer on team that works on all the critical dashboards and automation tool for pulling customer feedback from digital surveys, chat, and voice to be able to help digital teams address major pain points proactively to effectively and quickly address customers biggest pain points and help to reduce significant costs and call volumes in operations. Analyze and understand data sources & APIs Design and Develop methods to connect & collect data from different data sources. Design and Develop methods to filter/cleanse the data Design and Develop SQL , Hive queries, APIs to extract data from the store. Work closely with data scientists to ensure the source data is aggregated and cleansed. Work with product managers to understand the business objectives. Work with cloud and data architects to define robust architecture in cloud setup pipelines and work flows. Work with DevOps to build automated data pipelines.

  • Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
  • Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
  • Assess the effectiveness and accuracy of new data sources and data gathering techniques.
  • Develop custom data models and algorithms to apply to data sets.
  • Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
  • Develop company A/B testing framework and test model quality.
  • Coordinate with different functional teams to implement models and monitor outcomes.
  • Develop processes and tools to monitor and analyze model performance and data accuracy.

Brief Summary of Skills

  • Machine Learning Engineer requires both software engineering and data science experience.
  • Advanced degree in computer science, math, statistics or a related discipline
  • Extensive data modeling and data architecture skills
  • Programming experience in Python, R or Java
  • Background in machine learning frameworks such as TensorFlow or Keras
  • Advanced math skills (linear algebra, Bayesian statistics, group theory)
  • Strong written and verbal communications
  • Experience working in an Agile environment
  • Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
  • Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.

Compentencies:

  • Strong problem solving skills with an emphasis on product development.
  • Experience using statistical computer languages R, Python, SLQ, etc. to manipulate data and draw insights from large data sets.
  • Computer Science Fundamentals & programming – Knowledge of data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.), computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.), and computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.).
  • Knowledge of Probability & Statistics: Formal characterization of probability (conditional probability, Bayes’ rule, likelihood, independence) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models). Statistics measures (mean, median, variance), distributions (uniform, normal, binomial, Poisson), and analysis methods (ANOVA, hypothesis testing).
  • Must have knowledge and experience with standard implementations of machine learning algorithms through libraries, packages, and APIs (such as scikit-learn, Theano, Spark MLlib, H2O, and TensorFlow) and applying them effectively by selecting the right model (decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models) and a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods)
  • Need to understand how larger ecosystem of products and services work together, communicate with them (using library calls, REST APIs, database queries, etc.) and build appropriate interfaces that others will depend on. Need to utilize careful system design to avoid bottlenecks and allow algorithms to scale well with increasing volumes of data.
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