Is Senior Specialist, Data Analytics And Ai

Position: IS Senior Specialist, Data Analytics and AIDepartment: Aptar Information SystemsLocation: Aptar worldwideTravel Expectations: Up to 25%Reports To: Director, IS Data & AnalyticsPrimary Purpose SummaryThe 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, alongside strong problem-solving and communication skills.In this position he/she will be directly reporting to the Director, IS Business Analytics.Job Responsibilities:The IS Senior Specialist, Data Analytics and AI plays a key role in the end-to-end design and implementation of Aptar Machine Learning and AI use cases, works closely with both Data & Analytics team and business to identify key areas of development for Machine Learning and generative AI solutions, and has strong end-to-end solution ownership, translating product requirements into user interfaces and backend distributed system design as well as own the implementation of these designs.She/he is a senior team member of the projects and the programs in the area of Artificial Intelligence and engages stakeholders based upon the needs of his domainCollaboration & Stakeholder EngagementShe/he is independent & effectiveShe/he solve problems with Data, ML and AI, and recommends solutions to complex problems guided by business objectivesShe/he influences Aptar expert stakeholdersWork 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 & EngineeringPreprocess, 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 & OptimizationS elect, 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 & ValidationE valuate 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 & MonitoringC ollaborate 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 LearningS tay 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 & EthicsE nsure 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 PracticesDocument 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 SharingMentor 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 & IntegrationWork 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 QualificationsProgramming SkillsProficiency 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 FrameworksSupervised, 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 Expertiserong foundation in linear algebra, calculus, probability, and statistics.Familiarity with Bayesian statistics and probabilistic graphical models.Data Handling and Analysisata 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 Systemsxperience 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)entiment analysis, named entity recognition, text summarization.Knowledge of frameworks for NLP and text analysis.Optimization and Model Performanceyperparameter tuning techniques (e.g., Bayesian optimization).Feature selection and dimensionality reduction.Knowledge of anomaly detection algorithms.Model Deployment and Monitoringxpertise 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 Practicesoftware testing methodologies.Agile and Scrum project management methodologies.Visualization and CommunicationMatplotlib, Plotly, Power BI.Effective communication skills for both technical and non-technical audiences.Specialized TechniquesGraph analytics and neural networks.Time series analysis and forecasting (e.g., ARIMA, LSTM, Prophet).Knowledge of federated learning and differential privacy.Additional Skillsassion 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.EducationBachelor's Degree (Fundamentals)Core areas: Programming, algorithms, data structures, and computer systems.Mathematics: Linear algebra, calculus, probability, and statistics.Experience5+ years of experience with Proven experience of leading AI/ML initiatives and driving Innovations.#J-*****-Ljbffr