AI Summary
Prodapt seeks a Senior Machine Learning Engineer to develop predictive analytics solutions for customer churn using AI/ML techniques. The role involves building end-to-end ML pipelines, deploying models in production, and creating business intelligence reports. Candidates must have 3+ years of experience with GenAI, AI/ML, statistical modeling, MLOps, and data engineering tools.
Key Highlights
AI-first strategic technology partner for telecom enterprises
Build ML models for customer churn prediction using RapidMiner and Python
End-to-end ML deployment and data versioning in production
3+ years experience with GenAI, AI/ML, and MLOps technologies
Key Responsibilities
Perform exploratory data analysis and provide insights into customer data using domain knowledge
Leverage predictive analytics and AI/ML techniques to generate actionable insights for customer behavior, operational trends, and churn management
Study key data from customer, inventory, network and trouble management systems and provide recommendations on solutions
Build data ingesting pipelines and maintain them in big data ecosystems
Correlate analysis with real-time data from customer database using churn data
Design, build, test, and tune machine learning models using Python and other tools
Build ML models in RapidMiner application to predict customer churn using Python scripts
Determine initial set of potential ML models based on data and results generated
Maintain and suggest tools and technologies for increased productivity
Build and update business intelligence reports, databases, and dashboards
Architect end-to-end ML model deployment and data versioning pipeline in production
Participate in model and algorithm deployment into production with monitoring and alerting
Align code branches with latest algorithms for customer churn predictability
Assure adherence to business intelligence standards, methodologies, and practice
Maintain project codes/model versions using GIT/SVN version controlling tools
Document project details and activities in Confluence pages
Create status reports on weekly and monthly basis
Work with team on AI/ML technologies and business intelligence systems
Perform tests and work with project managers on deliverables
Attend project meetings and work on ad hoc project report requests
Triage requirement gathering and identify business value for scenarios
Optimize data-driven decision making
Explore and integrate AI/ML and GenAI frameworks for customer communications and operational insights
Utilize statistical concepts/methodologies to correlate inventory, network statistics, and trouble management systems
Technical Skills Required
Benefits & Perks
Telecommuting permitted from anywhere in the U.S.
Domestic travel approximately 10% of time to client sites
Job Description
Overview
Prodapt is the largest specialized player in the Connectedness industry. As an AI-first strategic technology partner, Prodapt provides consulting, business reengineering, and managed services for the largest telecom and tech enterprises building networks and digital experiences of tomorrow. A ServiceNow-invested company, Prodapt has been recognized by Gartner as a Large, Telecom-Native, Regional IT Service Provider. A “Great Place To Work® Certified™” company, Prodapt employs over 6,000 technology and domain experts across the Americas, Europe, India, Africa, & Japan. Prodapt is part of the 130-year-old business conglomerate The Jhaver Group, which employs over 32,000 people across 80+ locations globally.
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.
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- 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.
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