Infor’s Global Enablement C Education organization is seeking an experienced and dynamic Senior Learning Data Scientist. You will lead AI-driven analytics, predictive modeling, and data-automation initiatives that deliver actionable insights for training, certification, and credentialing programs. Your role will blend deep technical expertise, strategic vision, data storytelling, and leadership to influence business decisions and drive our Learning C Education analytics roadmap.
As a Senior Learning Data Scientist, you will:
- Architect, develop, and maintain advanced analytics solutions, dashboards, and predictive models.
- Drive AI and machine-learning projects end-to-end, from data ingestion through deployment.
- Lead cross-functional collaboration to gather requirements, define KPIs, and translate business needs into data solutions.
- Mentor and guide junior data scientists and analysts, fostering best practices in coding, modeling, and visualization.
- Champion data-driven decision making and influence stakeholders at all levels.
- Partner with Enablement C Education stakeholders to define analytics strategy, data instrumentation, and reporting requirements.
- Lead strategic data science projects: design experiments, prototypes, and production- grade ML/AI models.
- Build and deploy predictive models, propensity analyses, recommendation engines, and NLP solutions using Python, R, TensorFlow, PyTorch, scikit-learn, and/or Keras.
- Develop ETL/data pipelines on platforms like Apache Spark, Databricks, Airflow, or AWS Glue; manage data ingestion from LMS, CRM, SFDC, SharePoint, and internal APIs.
- Design and implement data models and star schemas in data warehouses (Snowflake, Redshift, BigǪuery) and data lakes (HDFS, S3).
- Create interactive dashboards and reports with Power BI, Tableau, Looker, ǪlikSense or Domo; embed analytics into web applications via JavaScript and RESTful APIs.
- Automate analytics workflows and ML pipelines with tools like MLflow, Kubeflow, Docker, CI/CD (Azure DevOps, Jenkins, GitLab).
- Utilize big-data and distributed computing frameworks (Hadoop, Hive, Presto, Spark) for large-scale data processing.
- Apply statistical analysis, A/B testing, and experimental design to validate model performance and business impact.
- Oversee model monitoring, versioning, and retraining; ensure data quality, security, and compliance.
- Convert legacy BI content (Business Objects, Cognos) into modern, user-friendly solutions.
- Provide technical leadership and mentoring: review code, share best practices, and lead training sessions.
- Communicate complex analyses and insights clearly and persuasively to executive leadership, partners, and non-technical stakeholders.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or related field.
- 8+ years of experience in data science, analytics, or BI within IT or related industries.
– Programming: Python, R, SǪL (MySǪL, SǪL Server, PostgreSǪL).
– ML/DL frameworks: TensorFlow, PyTorch, scikit-learn, Keras.
– Big data tools: Spark, Hadoop ecosystem (Hive, HDFS, Presto).
– Data warehouses/lakes: Snowflake, Redshift, BigǪuery, AWS S3.
– BI/Visualization: Power BI, Tableau, Looker, ǪlikSense, Domo.
– ETL/Orchestration: Apache Airflow, AWS Glue, Azure Data Factory.
– ML Ops: Docker, Kubernetes, MLflow, Kubeflow; CI/CD pipelines (Git, Azure DevOps, Jenkins).
– Scripting C Automation: Bash, PowerShell, RPA (UiPath, Automation Anywhere, Zapier).
– Cloud Platforms: AWS, Azure, GCP (compute, storage, ML services).
- Experience with data modeling, star schemas, OLAP cubes (SSAS), and dimensional design.
- Skilled in advanced statistics, hypothesis testing, segmentation, time-series forecasting, NLP, and recommendation systems.
- Familiarity with file formats (JSON, XML, Parquet, Avro) and data serialization.
- Understanding of data governance, privacy regulations (GDPR), and security best practices.
