Senior Data Scientist

Key Skills

Java Programming, Design, Python Programming, Database, SQL, RDBMS, Apache Hadoop, Machine learning techniques, Data science techniques, Apache Cassandra, Deep Learning, Keras software library, NoSQL, Pandas, PyTorch, TensorFlow, XGBoost, Apache Hive, Snowflake, Scikit-learn, Apache Impala, Statistics, PySpark, AWS Sagemaker, H2O, Data Analysis, IBM Watson Suite, Dask - Python library

Job Description

Fiserv is in the process of driving a strategic transformation by leveraging our unique position as an industry leader in the end-to-end lifecycle of payment processing and our ability to capitalize on one of our most important assets – data. Driven by a series of investments in emerging areas and market adjacencies, Fiserv is a leader in defining how our customers, their consumers, and marketplaces leverage information to create an advantage in their business and activities. Driving change through continuous experimentation and integrating unique data-driven insights at the points where decisions are being made, Fiserv is building a common data fabric that connects people, places, and things – weaving together customer touchpoints in new ways that drive rich experiences unparalleled in the industry.

The Data and Analytics (D&A) organization is looking for individuals who can make an impact in our data-driven-transformation – leaders in their field who can readily move between the complex world of data, analytics and insights and able to bridge towards the use of information in a business context – translating that complexity into strategies, products, capabilities, and ideas that our customers and partners understand

What does a successful Senior Data Scientist do at Fiserv?

As a Senior Data Scientist within the data science team, you will design and develop inventive products & solutions leveraging machine learning and deep learning models and advanced statistical techniques to drive innovation targeting specific business objectives and create other business opportunities. You will work on a variety of use cases including but not limited to forecasting, segmentation, churn prediction, propensity scoring, natural language processing, optimization, fraud scoring and risk analysis. You will work with a high performance computing (HPC) platform and related tools, frameworks accelerators and software that enhance a data scientist’s work and, ultimately, you will work in a highly collaborative, team environment where you will collaborate with multiple stakeholders including business, product development, architecture, and engineering teams.

In this role, you will:

• Support end-to-end data science project activities including requirement analysis, explorative data analysis, data wrangling, feature engineering, algorithm selection, model fitting/validation/verification, visualization, hyper parameter tuning, inference analysis, model deployment.
• Use a High-Performance Computing (HPC) platform to build, test and deploy AI/ML/DL models and provide post deployment support including monitoring, troubleshooting, improving model efficiency, and scaling the solution within the HPC platform.
• Work with several ML/DL frameworks including Spark, DASK, sci-kit learn, Xgboost, TensorFlow, Keras, PyTorch, Pandas, PyArrow and H20.
• Assist in the communication of insights and the implementation of impactful data science solutions across the organization.

You will need to have:

• A bachelor’s degree in Economics, Data Science, Computer Science, Statistics, Applied Mathematics, or related field. Even better if you have a Master’s or PhD.
• 3+ years of experience as a Data Scientist or in a related role.
• Proficient in applying macroeconomic and microeconomic theories to industry cases, time series econometrics and related.
• Experience working with different algorithms using Python (ie. PySpark, PyArrow TensorFlow, scikit-learn, Xgboost, DASK, H2o) and or experience efficiently deploying AI/ML solutions in big data production systems on premise or AWS Cloud with consideration of resource utilization and costs.
• Experience with data platforms and applications such as Azure ML, AWS Sage Maker, IBM Watson, Databricks, Domino Data Labs.
• Knowledgeable in building, optimizing, and scaling NLP models that comb through copious amounts of data and experience with ontology/taxonomy, & complex unstructured data.
• Proficiency with statistical data analysis and machine learning techniques (e.g., SVM, regression, classification, clustering, time series).
• Working knowledge of Hadoop, Hive, Impala, Cassandra, RDBMS, SQL, Snowflake, Vertica & RDBMS, or NoSQL Databases