Data Science & Machine Learning Architect

Prodapt•Irving, TX

About The Position

Prodapt is the largest and fastest-growing specialized player in the Connectedness industry, recognized by Gartner as a Large, Telecom-Native, Regional IT Service Provider across North America, Europe and Latin America. With its singular focus on the domain, Prodapt has built deep expertise in the most transformative technologies that connect our world. Prodapt is a trusted partner for enterprises across all layers of the Connectedness vertical. Prodapt designs, configures, and operates solutions across their digital landscape, network infrastructure, and business operations – and craft experiences that delight their customers. Today, Prodapt’s clients connect 1.1 billion people and 5.4 billion devices, and are among the largest telecom, media, and internet firms in the world. Prodapt works with Google, Amazon, Verizon, Vodafone, Liberty Global, Liberty Latin America, Claro, Lumen, Windstream, Rogers, Telus, KPN, Virgin Media, British Telecom, Deutsche Telekom, Adtran, Samsung, and many more. A “Great Place To Work® Certified™” company, Prodapt employs over 6,000 technology and domain experts in 30+ countries across North America, Latin America, Europe, Africa, and Asia. Prodapt is part of the 130-year-old business conglomerate The Jhaver Group, which employs over 30,000 people across 80+ locations globally. We are seeking an experienced Data Science & Machine Learning Architect with 13+ years experience in Irving, Texas to lead the design, development, and deployment of scalable advanced analytics, machine learning, and AI solutions. This role will partner closely with business leaders, data engineers, and IT teams to transform complex data into actionable insights and enterprise-grade ML platforms. The ideal candidate has deep expertise in data science, ML architecture, cloud platforms, and MLOps, with a strong ability to translate business problems into robust technical solutions.

Requirements

13+ years of experience in data science, machine learning, analytics, or related fields.
5+ years in an architecture or technical leadership role.
Strong expertise in Python (required); experience with R or Scala is a plus.
Hands-on experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost.
Deep knowledge of ML system design, MLOps, and model lifecycle management.
Experience with cloud platforms (AWS, Azure, or GCP).
Solid understanding of SQL, data modeling, and data warehousing.
Excellent communication and stakeholder management skills.

Nice To Haves

Master’s or PhD in Computer Science, Data Science, AI, Statistics, or related field.
Experience with generative AI, LLMs, and prompt engineering.
Knowledge of responsible AI, model governance, and regulatory compliance.
Prior experience in industries such as finance, healthcare, retail, telecom, or supply chain.
Architecture certifications (AWS, Azure, GCP) are a plus.

Responsibilities

Define and own the end-to-end architecture for data science, machine learning, and AI solutions.
Design scalable, secure, and high-performance ML platforms and pipelines.
Establish best practices, standards, and frameworks for model development, deployment, and governance.
Evaluate and recommend tools, technologies, and cloud services for analytics and AI initiatives.
Lead the development of advanced machine learning models (supervised, unsupervised, NLP, time series, deep learning).
Guide teams on feature engineering, model selection, training, evaluation, and optimization.
Ensure model explainability, fairness, and compliance where applicable.
Review and approve model designs and code from data scientists.
Architect and implement MLOps pipelines for CI/CD, model versioning, monitoring, and retraining.
Collaborate with data engineering teams on data ingestion, transformation, and feature stores.
Ensure reliable deployment using containers, APIs, and orchestration tools.
Monitor model performance, drift, and operational metrics in production.
Design ML solutions on AWS, Azure, or GCP (e.g., SageMaker, Azure ML, Vertex AI).
Leverage big data technologies such as Spark, Databricks, Snowflake, or Hadoop.
Ensure security, scalability, and cost optimization across platforms