Data Science Engineer
Aperio Global, LLC, United States

We are seeking an experienced and passionate Data Science Engineer to join our team and play a pivotal role in designing, implementing, and optimizing strategic data initiatives. If you have a strong background in data architecture, ETL processes, and machine learning, along with a desire to contribute to impactful projects, we invite you to apply. At Aperio Global, you will be part of a forward-thinking team working on exciting data-driven projects that drive our company's success.

Responsibilities

• Lead the design and implementation of strategic data initiatives, including but not limited to enterprise data warehouse (EDW), master data, data governance, data quality, metadata management, and data marts.
• Oversee and maintain both automated and manual data integration and Data/ETL jobs, ensuring their successful execution, verifying results, and measuring performance.
• Design and implement an Airports Authority-wide metadata and information management program. Develop enterprise-level conceptual, logical, and physical data models, providing essential data architecture support to major application development projects within an agile environment.
• Implement and enforce program standards and procedures to effectively administer the data warehouse. Offer technical expertise on data management and design to cross-functional teams, including Application Development and Enterprise Architecture project teams, end-users, and business stakeholders.
• Create robust designs and execute Data/Extract, Transform, and Load (ETL) coding for Source Dependent Extracts (Relational DB, APIs, flat files, etc.), Source Independent Loads, and Post-Load Processes. Ensure smooth data flow from source to target systems, including operational data stores and dimensional data warehouses, utilizing in-memory or Massively Parallel Processing platforms, or Structure Query Language (SQL) Server/Oracle Data Warehouses. Leverage near real-time loads and Change Data Capture techniques.
• Advanced Data Processing and Machine Learning: Extract valuable insights through data mining from diverse sources. Utilize machine learning tools to select features, optimize classifiers, and conduct preprocessing of structured and unstructured data.
• Enhance data collection procedures to comprehensively capture all pertinent information necessary for the development of cutting-edge analytic systems.
• Ensure data integrity by meticulously processing, cleansing, and validating data for analysis.
• Analyze vast datasets to uncover patterns, trends, and innovative solutions. Develop predictive systems and machine learning algorithms to generate actionable insights.
• Lead end-to-end execution of machine learning projects, encompassing understanding business requirements, data aggregation, exploratory analysis, model creation, validation, and deployment. Implement concept-drift monitoring and retraining strategies to continually enhance model performance.
• Stay updated with the latest advancements in machine learning and artificial intelligence. Research and implement novel ML approaches using a variety of AI services, platforms, and frameworks, such as TensorFlow, PyTorch, H2O.ai, SparkML, scikit-learn, MXNet, Azure Synapse Analytics, Google BigQuery, and others.

Requirements

• Bachelor’s degree in computer science, Data Science, Statistics, or a related field.
• Eight (8) years of progressively responsible experience in data warehousing and integration, including experience applying data modeling techniques and ETL coding.
• Knowledge of and skill in developing and implementing a corporate data architecture program with responsibility for enterprise conceptual/logical data modeling, data policies, standards and compliance monitoring, metadata mapping, data governance, and as-is/target data architecture to participate in strategic data initiatives.
• Knowledge of and skill in working with database management systems, such as Oracle, Redshift, MySQL, Snowflake and Microsoft Structured Query Language (SQL), and data warehouse solutions, and ETL tools (like Informatica Power Center, IICS) and reporting tools (like Qlik Sense, Tableau and PowerBI) to centrally manage and analyze data originating from disparate source systems.
• Programming Skills knowledge of statistical programming languages like R, Python, and database query languages like SQL, Hive, Pig is desirable. Familiarity with Scala, Java, or C++ is an added advantage.
• Statistics Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators, etc. Proficiency in statistics is essential for data-driven companies.
• Machine Learning good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests.
• Strong Math Skills (

Job Rewards and Benefits