Lead Engineer (Data Science)

Job Title: Lead Data Science Engineer Tax Term: W2/1099 Location: Hybrid - Dallas, TX Employment Type: Contract Duration: 12+ About Us: ConglomerateIT is a certified and a pioneer in providing premium end-to-end Global Workforce Solutions and IT Services to diverse clients across various domains. Visit us at http://www.conglomerateit.com Our mission is to establish global cross culture human connections that further the careers of our employees and strengthen the businesses of our clients. We are driven to use the power of global network to connect business with the right people without bias. We provide Global Workforce Solutions with affability. Job Summary: As a Lead Data Science Engineer, you’ll lead end-to-end data science initiatives from ideation and model development to production deployment, focusing on telecom network analytics, churn prediction, real-time monitoring, and service optimization. You’ll guide a cross-functional team of data scientists, engineers, and domain specialists to deliver scalable, data-driven solutions using advanced analytics, big data technologies, and cloud platforms. Key Responsibilities • Design, develop, and deploy data science models addressing key telecom challenges such as customer churn, QoS, SINR, and Video on Demand analytics. • Lead data engineering initiatives to build robust data pipelines from telecom wireline/wireless networks and real-time data streams. • Oversee project timelines, resources, and deliverables while ensuring alignment with telecom business objectives and KPIs. • Drive collaboration between data science, engineering, and telecom domain teams to promote innovation and efficient problem-solving. • Establish and enforce best practices for model governance, versioning, testing, and code quality. • Communicate project insights, outcomes, and recommendations effectively to both technical and business stakeholders. Qualifications & Skills • Proven leadership experience in telecom data science or analytics projects. • Strong expertise in statistical modeling, machine learning, and data engineering applied to telecom datasets. • Proficiency in Python, SQL, and big data frameworks such as Spark and Hadoop. • In-depth understanding of telecom metrics and data including wireline, wireless, NQES, churn, and real-time performance data. • Hands-on experience with Airflow, DataProc (GCP), Vertex AI, BigQuery, and Teradata. • Excellent communication, organization, and leadership abilities. • Familiarity with H2O is a plus.