Senior Specialist, Data Analytics and AI

Position: IS Senior Specialist, Data Analytics and AI

Position
: IS Senior Specialist, Data Analytics and AI

Department
:
Aptar Information Systems

Location
:
Aptar worldwide

Travel Expectations
:
Up to 25%

Reports To
:
Director, IS Data & Analytics

Primary Purpose Summary

The IS Senior Specialist, Data Analytics and AI is the key contributor to our machine learning initiatives, will manage the full development lifecycle, including data preprocessing, feature engineering, model training, deployment, and monitoring. She/he is a Subject Matter Expert in ML and AI, obtained through advanced technical education & work experience, interprets internal or external issues, and recommends solutions and best practices.

She/he will work with cross‑functional teams to analyze large datasets, build predictive models, and optimize algorithm performance.

This role offers the chance to work with advanced technologies and collaborate with talented professionals' team that value collaboration, continuous learning in a dynamic, innovative environment.

This role requires expertise in ML and AI algorithms, programming, and data analysis, along with strong problem‑solving and communication skills.

In this position he/she will be directly reporting to the Director, IS Business Analytics.

Job Responsibilities Collaboration & Stakeholder Engagement

• She/he is independent & effective
• She/he solve problems with Data, ML and AI, and recommends solutions to complex problems guided by business objectives
• She/he influences Aptar expert stakeholders
• Work with data scientists, software engineers, and business stakeholders to define problems, requirements, and objectives.
• Collaborate with domain experts to gather insights for enhancing model relevance and performance.
• Communicate findings, results, and recommendations effectively to both technical and non‑technical stakeholders.
• Participate in cross-functional discussions to identify business problems and opportunities for machine learning solutions.

Data Preparation & Engineering

• Preprocess, clean, and normalize large datasets to ensure data quality.
• Conduct exploratory data analysis to understand patterns and distributions.
• Engineer and select relevant features to optimize model performance.
• Develop and maintain scalable data pipelines for ingestion, transformation, and feature engineering.

Model Development & Optimization

• Select, implement, and fine‑tune appropriate machine learning algorithms or Gen AI models.
• Train models, adjust hyperparameters, and optimize algorithms for performance.
• Apply advanced techniques such as transfer learning, ensemble learning, and data augmentation.
• Optimize models for resource‑constrained environments (e.g., edge or IoT devices).

Model Evaluation & Validation

• Evaluate models using appropriate metrics and validate against test datasets.
• Conduct experiments (e.g., A/B testing) to assess model impact on business metrics.
• Benchmark different algorithms to select the most suitable approach.

Deployment & Monitoring

• Collaborate with software engineers and Dev Ops teams to deploy machine learning models.
• Develop monitoring systems to track performance, detect anomalies, and implement updates.
• Ensure scalability, reliability, and performance in production environments.

Research & Continuous Learning

• Stay updated with advancements in machine learning, AI frameworks, and tools.
• Explore new methodologies, algorithms, and frameworks to improve workflows.
• Participate in professional development activities, such as conferences and workshops.

Compliance & Ethics

• Ensure compliance with data privacy and security regulations when handling sensitive data.
• Implement techniques for model fairness, explainability, and interpretability.
• Collaborate with data governance teams to adhere to ethical guidelines and regulatory requirements.

Documentation & Best Practices

• Document machine learning models, processes, and workflows to ensure reproducibility.
• Maintain version control for tracking changes in code and experiments.
• Contribute to developing and maintaining reusable components and frameworks.

Mentorship & Knowledge Sharing

• Mentor junior team members and provide technical guidance.
• Share knowledge through blog posts, open-source projects, and community…