PpbPosition /b: IS Senior Specialist, Data Analytics and AI /p pbDepartment /b: Aptar Information Systems /p pbLocation /b: Aptar worldwide /p pbTravel Expectations /b: Up to 25% /p pbReports To /b: Director, IS Data Analytics /p h3Primary Purpose Summary /h3 pThe 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. /p pThis 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. /p pThis role requires expertise in ML and AI algorithms, programming, and data analysis, alongside strong problem-solving and communication skills. /p pIn this position he/she will be directly reporting to the Director, IS Business Analytics. /p h3Job Responsibilities: /h3 pThe 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. /p pShe/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 domain /p h3Collaboration Stakeholder Engagement /h3 ulliShe/he is independent effective /liliShe/he solve problems with Data, ML and AI, and recommends solutions to complex problems guided by business objectives /liliShe/he influences Aptar expert stakeholders /liliWork with data scientists, software engineers, and business stakeholders to define problems, requirements, and objectives. /liliCollaborate with domain experts to gather insights for enhancing model relevance and performance. /liliCommunicate findings, results, and recommendations effectively to both technical and non-technical stakeholders. /liliParticipate in cross-functional discussions to identify business problems and opportunities for machine learning solutions. /li /ul h3Data Preparation Engineering /h3 ulliPreprocess, clean, and normalize large datasets to ensure data quality. /liliConduct exploratory data analysis to understand patterns and distributions. /liliEngineer and select relevant features to optimize model performance. /liliDevelop and maintain scalable data pipelines for ingestion, transformation, and feature engineering. /li /ul h3Model Development Optimization /h3 ulliS elect, implement, and fine-tune appropriate machine learning algorithms or Gen AI models. /liliTrain models, adjust hyperparameters, and optimize algorithms for performance. /li /ul ulli•Apply advanced techniques such as transfer learning, ensemble learning, and data augmentation. /liliOptimize models for resource-constrained environments (e.g., edge or IoT devices). /li /ul h3Model Evaluation Validation /h3 ulliE valuate models using appropriate metrics and validate against test datasets. /liliConduct experiments (e.g., A/B testing) to assess model impact on business metrics. /liliBenchmark different algorithms to select the most suitable approach. /li /ul h3Deployment Monitoring /h3 ulliC ollaborate with software engineers and DevOps teams to deploy machine learning models. /liliDevelop monitoring systems to track performance, detect anomalies, and implement updates. /liliEnsure scalability, reliability, and performance in production environments. /li /ul h3Research Continuous Learning /h3 ulliS tay updated with advancements in machine learning, AI frameworks, and tools. /liliExplore new methodologies, algorithms, and frameworks to improve workflows. /liliParticipate in professional development activities, such as conferences and workshops. /li /ul h3Compliance Ethics /h3 ulliE nsure compliance with data privacy and security regulations when handling sensitive data. /liliImplement techniques for model fairness, explainability, and interpretability. /li /ul ulli•Collaborate with data governance teams to adhere to ethical guidelines and regulatory requirements. /li /ul h3Documentation Best Practices /h3 ulliDocument machine learning models, processes, and workflows to ensure reproducibility. /liliMaintain version control for tracking changes in code and experiments. /liliContribute to developing and maintaining reusable components and frameworks. /li /ul h3Mentorship Knowledge Sharing /h3 ulliMentor junior team members and provide technical guidance. /liliShare knowledge through blog posts, open-source projects, and community contributions. /liliParticipate in knowledge-sharing sessions within the organization. /li /ul h3Cross-functional Collaboration Integration /h3 ulliWork with data engineers to optimize data infrastructure and pipelines. /liliCollaborate with business stakeholders to integrate machine learning into existing systems. /liliContribute to building company-wide machine learning infrastructure. /li /ul h3Required Skills and Qualifications /h3 h3Programming Skills /h3 ulliProficiency in programming languages: Python, Spark, R, Java, SQL. /liliExperience with implementing machine learning algorithms and models. /liliFamiliarity with version control systems (e.g., Azure DevOps). /li /ul h3Machine Learning Algorithms and Frameworks /h3 ulliSupervised, unsupervised, and reinforcement learning. /liliMachine learning libraries: TensorFlow, PyTorch, scikit-learn, Keras. /liliNeural networks, CNNs, RNNs, GANs. /liliAutoML tools. /liliReinforcement learning frameworks like OpenAI Gym. /li /ul h3Mathematical and Statistical Expertise /h3 ulli brong foundation in linear algebra, calculus, probability, and statistics. /b /liliFamiliarity with Bayesian statistics and probabilistic graphical models. /li /ul h3Data Handling and Analysis /h3 ulli bata manipulation libraries: pandas, NumPy, SQL. /b /liliData preprocessing, feature engineering, and exploratory data analysis. /liliKnowledge of handling structured and unstructured data (e.g., text, images, audio, video). /li /ul h3Big Data and Distributed Systems /h3 ulli bxperience with big data technologies: /b /liliApache Spark, distributed computing frameworks (like Databricks, Dataiku…). /liliUnderstanding cloud-based services for data storage (e.g., Azure ADLS, Amazon S3, Google Cloud Storage). /li /ul h3Natural Language Processing (NLP) /h3 ulli bentiment analysis, named entity recognition, text summarization. /b /liliKnowledge of frameworks for NLP and text analysis. /li /ul h3Optimization and Model Performance /h3 ulli byperparameter tuning techniques (e.g., Bayesian optimization). /b /liliFeature selection and dimensionality reduction. /liliKnowledge of anomaly detection algorithms. /li /ul h3Model Deployment and Monitoring /h3 ulli bxpertise in deploying models using: /b /liliRESTful APIs, microservices architecture. /liliContainerization tools (e.g., Docker, Kubernetes). /liliSkills in model monitoring and drift detection. /liliUnderstanding of model interpretability techniques (e.g., SHAP, feature importance). /li /ul h3Software Engineering Best Practices /h3 ulli boftware testing methodologies. /b /liliAgile and Scrum project management methodologies. /li /ul h3Visualization and Communication /h3 ulliMatplotlib, Plotly, Power BI. /liliEffective communication skills for both technical and non-technical audiences. /li /ul h3Specialized Techniques /h3 ulliGraph analytics and neural networks. /liliTime series analysis and forecasting (e.g., ARIMA, LSTM, Prophet). /liliKnowledge of federated learning and differential privacy. /li /ul h3Additional Skills /h3 ulli bassion for continuous learning and staying updated with advancements. /b /liliAwareness of ethical considerations and data privacy in machine learning. /liliAbility to work collaboratively in cross-functional teams. /li /ul h3Education /h3 ulliBachelor’s Degree (Fundamentals) /liliCore areas: Programming, algorithms, data structures, and computer systems. /liliMathematics: Linear algebra, calculus, probability, and statistics. /li /ul h3Experience /h3 ulli5+ years of experience with Proven experience of leading AI/ML initiatives and driving Innovations. /li /ul /p #J-18808-Ljbffr