AptarGroup, Inc.
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 DevOps 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 contributions.
Participate in knowledge-sharing sessions within the organization.
Cross-functional Collaboration & Integration
Work with data engineers to optimize data infrastructure and pipelines.
Collaborate with business stakeholders to integrate machine learning into existing systems.
Contribute to building company-wide machine learning infrastructure.
Required Skills and Qualifications Programming Skills
Proficiency in programming languages: Python, Spark, R, Java, SQL.
Experience with implementing machine learning algorithms and models.
Familiarity with version control systems (e.g., Azure DevOps).
Machine Learning Algorithms and Frameworks
Supervised, unsupervised, and reinforcement learning.
Machine learning libraries: TensorFlow, PyTorch, scikit-learn, Keras.
Neural networks, CNNs, RNNs, GANs.
AutoML tools.
Reinforcement learning frameworks like OpenAI Gym.
Mathematical and Statistical Expertise
Strong foundation in linear algebra, calculus, probability, and statistics.
Familiarity with Bayesian statistics and probabilistic graphical models.
Data Handling and Analysis
Data manipulation libraries: pandas, NumPy, SQL.
Data preprocessing, feature engineering, and exploratory data analysis.
Knowledge of handling structured and unstructured data (e.g., text, images, audio, video).
Big Data and Distributed Systems
Experience with big data technologies: Apache Spark, distributed computing frameworks (like Databricks, Dataiku).
Understanding cloud-based services for data storage (e.g., Azure ADLS, Amazon S3, Google Cloud Storage).
Natural Language Processing (NLP)
Sentiment analysis, named entity recognition, text summarization.
Knowledge of frameworks for NLP and text analysis.
Optimization and Model Performance
Hyperparameter tuning techniques (e.g., Bayesian optimization).
Feature selection and dimensionality reduction.
Knowledge of anomaly detection algorithms.
Model Deployment and Monitoring
Expertise in deploying models using: RESTful APIs, microservices architecture.
Containerization tools (e.g., Docker, Kubernetes).
Skills in model monitoring and drift detection.
Understanding of model interpretability techniques (e.g., SHAP, feature importance).
Software Engineering Best Practices
Software testing methodologies.
Agile and Scrum project management methodologies.
Visualization and Communication
Matplotlib, Plotly, Power BI.
Effective communication skills for both technical and non-technical audiences.
Specialized Techniques
Graph analytics and neural networks.
Time series analysis and forecasting (e.g., ARIMA, LSTM, Prophet).
Knowledge of federated learning and differential privacy.
Additional Skills
Passion for continuous learning and staying updated with advancements.
Awareness of ethical considerations and data privacy in machine learning.
Ability to work collaboratively in cross-functional teams.
Education
Bachelor's Degree (Fundamentals). Core areas: Programming, algorithms, data structures, and computer systems.
Mathematics: Linear algebra, calculus, probability, and statistics.
Experience
5+ years of experience with Proven experience of leading AI/ML initiatives and driving Innovations.
#J-18808-Ljbffr
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 DevOps 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 contributions.
Participate in knowledge-sharing sessions within the organization.
Cross-functional Collaboration & Integration
Work with data engineers to optimize data infrastructure and pipelines.
Collaborate with business stakeholders to integrate machine learning into existing systems.
Contribute to building company-wide machine learning infrastructure.
Required Skills and Qualifications Programming Skills
Proficiency in programming languages: Python, Spark, R, Java, SQL.
Experience with implementing machine learning algorithms and models.
Familiarity with version control systems (e.g., Azure DevOps).
Machine Learning Algorithms and Frameworks
Supervised, unsupervised, and reinforcement learning.
Machine learning libraries: TensorFlow, PyTorch, scikit-learn, Keras.
Neural networks, CNNs, RNNs, GANs.
AutoML tools.
Reinforcement learning frameworks like OpenAI Gym.
Mathematical and Statistical Expertise
Strong foundation in linear algebra, calculus, probability, and statistics.
Familiarity with Bayesian statistics and probabilistic graphical models.
Data Handling and Analysis
Data manipulation libraries: pandas, NumPy, SQL.
Data preprocessing, feature engineering, and exploratory data analysis.
Knowledge of handling structured and unstructured data (e.g., text, images, audio, video).
Big Data and Distributed Systems
Experience with big data technologies: Apache Spark, distributed computing frameworks (like Databricks, Dataiku).
Understanding cloud-based services for data storage (e.g., Azure ADLS, Amazon S3, Google Cloud Storage).
Natural Language Processing (NLP)
Sentiment analysis, named entity recognition, text summarization.
Knowledge of frameworks for NLP and text analysis.
Optimization and Model Performance
Hyperparameter tuning techniques (e.g., Bayesian optimization).
Feature selection and dimensionality reduction.
Knowledge of anomaly detection algorithms.
Model Deployment and Monitoring
Expertise in deploying models using: RESTful APIs, microservices architecture.
Containerization tools (e.g., Docker, Kubernetes).
Skills in model monitoring and drift detection.
Understanding of model interpretability techniques (e.g., SHAP, feature importance).
Software Engineering Best Practices
Software testing methodologies.
Agile and Scrum project management methodologies.
Visualization and Communication
Matplotlib, Plotly, Power BI.
Effective communication skills for both technical and non-technical audiences.
Specialized Techniques
Graph analytics and neural networks.
Time series analysis and forecasting (e.g., ARIMA, LSTM, Prophet).
Knowledge of federated learning and differential privacy.
Additional Skills
Passion for continuous learning and staying updated with advancements.
Awareness of ethical considerations and data privacy in machine learning.
Ability to work collaboratively in cross-functional teams.
Education
Bachelor's Degree (Fundamentals). Core areas: Programming, algorithms, data structures, and computer systems.
Mathematics: Linear algebra, calculus, probability, and statistics.
Experience
5+ years of experience with Proven experience of leading AI/ML initiatives and driving Innovations.
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