Software Development Engineering - Advisor II

Calling all innovators - find your future at Fiserv.

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Job Title

TITLE:                                          Software Development Engineering - Advisor II

EMPLOYER:                               Fiserv Solutions, LLC

LOCATION:                                 Milwaukee, WI (and various unanticipated locations throughout the U.S. subject to authorization from management)

DUTIES:                                      Spearhead and fine tune the creation of emerging deep learning models that transform text, raw images, and time series data into interpretable information and actionable recommendations to power AI applications. Design and implement user-friendly interactive web applications that connect with machine learning systems. Deploy deep learning models. Design and implement dashboards to summarize key analytics for stakeholders. Collaborate with product, engineering, and operational teams to proactively gather feedback, gather requirements, and develop metrics. Communicate findings, recommendations, and analysis results to stakeholders in verbal, visual and written media. Stay up-to-date with industry trends and advancements in machine learning platforms as well as machine learning techniques, and lead the team in learning and improvement.

REQUIREMENTS:                     Bachelor's degree in Computer Engineering, Telecommunications Engineering, Electrical Engineering, Mechanical Engineering, Computer Science, Data Science, Statistics, Mathematics, Applied Mathematics, Physics, or a related field and 4 years in any job title involving algorithm, machine learning framework, and platform development experience in data science. Alternatively, the employer will accept Master’s degree in Computer Engineering, Telecommunications Engineering, Electrical Engineering, Mechanical Engineering, Computer Science, Data Science, Statistics, Mathematics, Applied Mathematics, Physics, or a related field and 2 years in any job title involving algorithm, machine learning framework, and platform development experience in data science. Prior experience must include: 2 years developing and deploying data science models using Python; 2 years working with version control systems including GIT; 2 years documenting the development processes, including code, design decisions, and user guides; 2 years working in Machine Learning (ML), natural language processing (NLP), and deep learning (DL) models for business specific use cases; 1 year designing and building topic modeling algorithms using Latent Dirichlet Allocation (LDA); Designing and creating Sigma dashboard to showcase topic modeling results for stakeholders; Performing analysis on topic modeling outputs to derive actionable insights and present findings to stakeholders; Presenting highly technical findings to non-technical audience including senior leadership; Performing Exploratory Data Analysis (EDA) on numerical and categorical features to identify relevant features; Designing and developing ensemble model to predict merchant attrition risk; Designing, developing and deploying merchant retention models to predict customer retention using Adobe Analytics; Designing and developing clustering algorithm on a set of customers identified by merchant retention model based on heuristics and business requirements; Designing KPIs based on customer behavior data to measure performance and customer stickiness; Designing and building database structure and automated reporting dashboard using tableau and sigma; Designing and developing Random Forest and Regression model to predict customer/merchant retention; Designing and developing time series model using ARIMA and fbProphet for multi-region demand forecasting at multiple temporal resolution; Working with TensorFlow, scikit-learn, and Keras for machine learning tasks; Implementing web scraping and data mining solutions to gather open-source data using scrapy in Python; Leading and performing architectural design with collaborators based on stakeholder requirements; and Performing text preprocessing, vectorization using Tf-Idf, clustering using DBScan, and summarization methods.

TRAVEL:                                     None

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