Bachelor's degree in Computer Science, Data Sciences, or Information Systems.
3 years of experience with GenAI Technologies like LLMs and Prompt Engineering.
3 years with AI/ML Technologies including Scikit-learn and TensorFlow.
3 years of experience with Statistical Modeling & ML Algorithms.
3 years with MLOps & Deployment including Docker and Kubernetes.
3 years with API & Backend Development using REST APIs.
3 years with Data Engineering & Orchestration tools like Apache Airflow.
Responsibilities
Perform exploratory data analysis to derive insights from customer data.
Leverage predictive analytics and AI/ML for actionable customer behavior insights.
Study datasets from various systems to recommend solution improvements.
Build and maintain data ingestion pipelines in big data environments.
Design, build, and tune machine learning models using Python.
Evaluate various ML algorithms for customer churn use cases.
Architect and implement end-to-end ML model deployment pipelines.
Benefits
Telecommuting and remote work from anywhere in the U.S. permitted.
Opportunities for travel and relocation to client locations across the U.S.
Approximately 10% domestic travel to various client sites.
Full Job Description
Responsibilities
Perform exploratory data analysis and provide various insights into customer data using domain knowledge that would bring more value to the business.
Leverage predictive analytics and AI/ML techniques to generate actionable insights for customer behavior, operational trends, and churn management.
Study key data from the customer, inventory, network and trouble management systems and provide recommendations on the solutions that can be built out of the provided dataset.
Build data ingesting pipelines and maintain them in big data ecosystems.
Correlate analysis with real-time data from the customer database using churn data.
Design, build, test, and tune machine learning models using Python and other tools, focusing on accuracy and ensuring that intelligence is consistent with defined needs.
Create solutions by comparing various Machine Learning algorithms that would best fit for the customer churn and use cases.
Build the Machine Learning models in tools such as RapidMiner application to predict customer churn using Python scripts.
Use algorithmic and logical approach to determine initial set of potential ML models based on the data and results generated.
Maintain and suggest tools and technologies for increased productivity.
Build and update business intelligence reports, databases, and dashboards to provide users with detailed intelligence.
Architect end-to-end Machine Learning Model Deployment and Data Versioning pipeline in a production environment to identify data patterns and trends.
Participate in model and algorithm deployment into production, which needs a separate pipeline built with support for monitoring and alerting.
Align code branches to be managed with the latest algorithms to be used for customer churn predictability.
Assure adherence to business intelligence standards, methodologies, and practice.
Maintain the project codes/ model versions using GIT/SVN version controlling tools.
Document all the project details and activities in the organization's Confluence pages.
Develop technical design documentation to ensure the accurate development of reporting solutions.
Create status reports on a weekly and monthly basis with an accurate assessment of the deliverables.
Work with the team on the various AI/ML technologies, and business intelligence systems and tools, perform tests, and work with project managers and team on project deliverables.
Attend project meetings and work on ad hoc project report requests.
Triage requirement gathering, identify business value for scenarios by working with product owners, and optimize data-driven decision making.
Utilize statistical concepts/methodologies to correlate inventory, network statistics, and trouble management systems.
Explore and integrate AI/ML and GenAI frameworks to enhance customer communications and operational insights, including IVR call analytics and AI-driven outage intelligence across digital and self-service channels.
Telecommuting and working from home permitted from anywhere in the U.S.
Travel and relocation possible to unanticipated client locations throughout the U.S.
Domestic travel required approximately 10% of the time to various client sites.
Requirements
Bachelor's degree or foreign equivalent in Computer Science, Data Sciences, or Information Systems and 3 years of experience in the job offered or 3 years of experience in the related occupations of Lead Engineer, Software Engineer, Application Developer, or equivalent.
Prior experience must include 3 years of experience with GenAI Technologies such as LLMs, Prompt Design, Prompt Engineering, LangChain, Hugging Face;
3 years with AI/ML Technologies such as Scikit-learn, TensorFlow, PyTorch, Keras, Pandas, NumPy, Spark ML, NLTK, H2O, AutoML, RapidMiner, Rasa, cuDNN;
3 years of experience with Statistical Modelling & ML Algorithms such as Regression, Time Series Analysis, Random Forests, Gradient Boosting, K-Means, KNN, Neural Networks;
3 years of experience with Model Evaluation & Testing such as Accuracy, Precision, Recall, Cross-Validation, A/B Testing, Hypothesis Testing;
3 years of experience with MLOps & Deployment such as Docker, Kubernetes (AKS), CI/CD, Model Monitoring, Data Versioning;
3 years of experience with API & Backend Development such as REST APIs, FastAPI, Uvicorn;
3 years of experience with Data Engineering & Orchestration such as Apache Airflow, Kafka, Flume, Hadoop, Drill;
3 years with Data Visualization & BI Tools such as Matplotlib, Seaborn, Grafana, Power BI, Tableau, Superset;
3 years with Databases & Data Warehouses such as Snowflake, PostgreSQL, Oracle, MySQL, HBase, Hive, SQL;
3 years with Cloud Technologies such as Azure (OpenAI, AI Search, ML), AWS (S3, Athena), GCP (Dialog flow, Data Studio);
3 years with DevOps & Collaboration Tools such as Git, Jira, Confluence, Azure DevOps;
3 years with Operating Systems such as Windows, Linux.
Travel and relocation possible to unanticipated client locations throughout the U.S.
Domestic travel required approximately 10% of the time to various client sites.
