HO - Data Science Specialist

Data Science is responsible for leading the data science function, driving data-driven decision-making, and delivering advanced analytics, machine learning, and AI solutions that support business objectives. This role combines strong technical expertise, leadership capabilities, and business acumen to manage a team of data scientists and collaborate with stakeholders across the organization.

Key Responsibilities

Advanced Analytics & Machine Learning

• Design and implement predictive, prescriptive, and descriptive analytics solutions.
• Develop machine learning, deep learning, and AI models to solve business challenges.
• Guide model selection, feature engineering, model training, and performance evaluation.
• Ensure models are scalable, reliable, and aligned with business needs.

Business Partnership

• Collaborate with business leaders to identify opportunities for data-driven improvements.
• Translate complex business problems into analytical frameworks and actionable solutions.
• Present insights, recommendations, and model outcomes to executive stakeholders.
• Drive adoption of analytics and AI across business functions.

Data Strategy & Governance

• Contribute to enterprise data strategy and analytics roadmap development.
• Ensure compliance with data governance, privacy, and security policies.
• Promote data quality, integrity, and accessibility across the organization.
• Work closely with Data Engineering and BI teams to establish trusted data platforms.

Project & Stakeholder Management

• Manage multiple analytics and AI projects simultaneously.
• Define project scope, timelines, resource requirements, and success criteria.
• Monitor project progress and communicate updates to stakeholders.
• Ensure projects are delivered on time and within budget.

Model Deployment & MLOps

• Collaborate with engineering teams to deploy machine learning solutions into production.
• Implement model monitoring, retraining, and lifecycle management processes.
• Drive adoption of MLOps frameworks and automation practices.
• Ensure model explainability, fairness, and performance tracking.

Innovation & Research

• Evaluate emerging AI, machine learning, and analytics technologies.
• Identify opportunities to leverage Generative AI and advanced analytics capabilities.
• Promote experimentation through proof-of-concept initiatives and pilot projects.
• Stay current on industry trends and technology advancements.

Required Qualifications

• Bachelor‘s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Economics, or a related field.
• Master‘s degree in Data Science, Artificial Intelligence, Analytics, Statistics, or a related discipline is preferred.
• At least 5 years of experience in Data Science, Machine Learning, Advanced Analytics, or AI.
• Experience delivering machine learning solutions in production environments.
• Proven track record of leading cross-functional analytics initiatives.
• Experience working with enterprise-scale data platforms and cloud environments.

Technical Skills

Data Science & Machine Learning

• Machine Learning Algorithms
• Deep Learning
• Natural Language Processing (NLP)
• Generative AI / Large Language Models (LLMs)
• Time Series Forecasting
• Recommendation Systems
• Statistical Modeling
• A/B Testing and Experimentation

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• ->

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"description":"Data Science is responsible for leading the data science function, driving data-driven decision-making, and delivering advanced analytics, machine learning, and AI solutions that support business objectives. This role combines strong technical expertise, leadership capabilities, and business acumen to manage a team of data scientists and collaborate with stakeholders across the organization.

Key Responsibilities

Advanced Analytics & Machine Learning

• Design and implement predictive, prescriptive, and descriptive analytics solutions.
• Develop machine learning, deep learning, and AI models to solve business challenges.
• Guide model selection, feature engineering, model training, and performance evaluation.
• Ensure models are scalable, reliable, and aligned with business needs.

Business Partnership

• Collaborate with business leaders to identify opportunities for data-driven improvements.
• Translate complex business problems into analytical frameworks and actionable solutions.
• Present insights, recommendations, and model outcomes to executive stakeholders.
• Drive adoption of analytics and AI across business functions.

Data Strategy & Governance

• Contribute to enterprise data strategy and analytics roadmap development.
• Ensure compliance with data governance, privacy, and security policies.
• Promote data quality, integrity, and accessibility across the organization.
• Work closely with Data Engineering and BI teams to establish trusted data platforms.

Project & Stakeholder Management

• Manage multiple analytics and AI projects simultaneously.
• Define project scope, timelines, resource requirements, and success criteria.
• Monitor project progress and communicate updates to stakeholders.
• Ensure projects are delivered on time and within budget.

Model Deployment & MLOps

• Collaborate with engineering teams to deploy machine learning solutions into production.
• Implement model monitoring, retraining, and lifecycle management processes.
• Drive adoption of MLOps frameworks and automation practices.
• Ensure model explainability, fairness, and performance tracking.

Innovation & Research

• Evaluate emerging AI, machine learning, and analytics technologies.
• Identify opportunities to leverage Generative AI and advanced analytics capabilities.
• Promote experimentation through proof-of-concept initiatives and pilot projects.
• Stay current on industry trends and technology advancements.

Required Qualifications

• Bachelor‘s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Economics, or a related field.
• Master‘s degree in Data Science, Artificial Intelligence, Analytics, Statistics, or a related discipline is preferred.
• At least 5 years of experience in Data Science, Machine Learning, Advanced Analytics, or AI.
• Experience delivering machine learning solutions in production environments.
• Proven track record of leading cross-functional analytics initiatives.
• Experience working with enterprise-scale data platforms and cloud environments.

Technical Skills

Data Science & Machine Learning

• Machine Learning Algorithms
• Deep Learning
• Natural Language Processing (NLP)
• Generative AI / Large Language Models (LLMs)
• Time Series Forecasting
• Recommendation Systems
• Statistical Modeling
• A/B Testing and Experimentation

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