Essential Functions
Building MLOps Pipelines: Develop, construct, test, and maintain architectures such as databases and large-scale processing systems to manage the ML lifecycle.
Developing and Retraining ML Models: Develop machine learning models and retrain existing models based on evolving data trends.
Implementing ML/AI on Large-Scale Datasets: Implement machine learning and AI models on large-scale datasets, including taking small scale models and customizing them for large-scale application without compromising performance.
Model Lifecycle Management: Manage the end-to-end lifecycle of Machine Learning models, including deployment, monitoring, and versioning.
Creating Data Pipelines: Create scalable and real-time data pipelines for machine learning workflows.
Collaboration: Collaborate with data scientists and ML engineers to understand their needs and assist with ML lifecycle processes.
Stakeholder Management: Work closely with stakeholders across the organization to understand their needs and deliver solutions that meet their requirements.
Continuous Improvement: Continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers.
Technical Skills:
Proficiency in Machine Learning, Deep Learning, and related technologies.
Experience with big data tools: Hadoop, Hive, PySpark, Apache Spark, MLlib, and GraphX.
Experience with data pipeline and workflow management tools like Airflow, etc.
Experience working in building and integrating the code in the defined CI/CD framework using git.
Proficient in some or all of the following techniques - Linear & Logistic Regression, Decision Trees, XG Boost, Random Forests, K-Nearest Neighbors, Markov Chain, Monte Carlo, Gibbs Sampling, Evolutionary Algorithms, Support Vector Machines.
Proficient in advanced data mining and statistical modeling techniques, including Predictive modeling (e.g., binomial, and multinomial regression, ANOVA), Classification techniques (e.g., Clustering, Principal Component Analysis, factor analysis), Decision Tree techniques (e.g., CART, CHAID).
Experience working with large scale data ingestion, processing, and storage in distributed computing environments / big data platforms (Hadoop) as well as common database systems and value stores (Parquet, Avro, HBase, etc.).
Familiarity with Python-based data science libraries: iPython, sci-kit learn, Pandas.
Familiarity with both common computing environments (e.g., Linux, Shell Scripting) and commonly used IDE’s (Jupyter Notebooks).
Knowledge of SQL and other data manipulation languages.
Understanding of data warehousing and ETL techniques.
Experience with Python or R for data analysis is a plus.
Familiarity with Agile development methodologies.
Strong quantitative skills, with a degree in a field such as Statistics, Mathematics, Computer Science, or related field.
Other Skills
Strong problem-solving skills.
Excellent communication skills.
Ability to work in a team.
Detail-oriented and excellent organizational skills.
Strong understanding of machine learning principles and best practices.
Ability to translate complex data into understandable, actionable insights.
This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.
Basic Qualifications
•5 or more years of relevant work experience with a Bachelor's Degree or at least 2
years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD)
or 0 years of work experience with a PhD
Preferred Qualifications
•6 or more years of work experience with a Bachelor's Degree or 4 or more years of
relevant experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or up
to 3 years of relevant experience with a PhD
•Exposure to Financial Services/ Payments Industry
Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.
