Job Description

ROLES & RESPONSIBILITIES Architecture Design: Design and develop robust, scalable, and secure data science architectures that align with business requirements and objectives. Define the overall technical vision and strategy for data science projects and initiatives. Solution Development: Lead the development and implementation of end-to-end data science solutions, including data ingestion, preprocessing, feature engineering, modeling, evaluation, and deployment. Collaborate with cross-functional teams, including data engineers, software developers, and domain experts, to ensure successful project delivery. Machine Learning and AI: Utilize your expertise in machine learning and artificial intelligence to guide the selection and application of appropriate algorithms and models. Stay up-to-date with the latest advancements in the field and identify opportunities for integrating new techniques into existing solutions. Data Governance: Ensure data quality, integrity, and security throughout the data science lifecycle. Establish and enforce data governance policies, standards, and best practices to ensure compliance with regulations and industry standards. Performance Optimization: Identify performance bottlenecks and propose optimization strategies to enhance the efficiency and scalability of data science workflows. Conduct thorough analysis and profiling of large datasets to optimize data processing and model training times. Collaboration and Leadership: Act as a subject matter expert and provide technical guidance to data science teams. Foster a collaborative work environment, mentor junior team members, and promote knowledge sharing and best practices across the organization. Research and Innovation: Stay abreast of emerging trends, tools, and methodologies in data science, machine learning, and artificial intelligence. Conduct research and evaluate new technologies to drive innovation and improve the overall data science capabilities of the organization. Stakeholder Engagement: Collaborate with stakeholders, including business leaders, executives, and clients, to understand their requirements and translate them into actionable data science solutions. Communicate complex technical concepts to non-technical audiences effectively.

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