AI Engineer

As a Data Scientist with Advanced Analytics skills, you will leverage deep data and analytics expertise with strong business acumen to address business challenges. You will utilize data preparation, analysis, and predictive modeling to forecast trends and suggest optimizations for improved business outcomes. Your primary responsibilities will include:

• Develop Predictive Models: Utilize mathematical optimization, discrete-event simulation, rules programming, and predictive analytics to forecast trends and suggest optimizations for improved business outcomes.
• Analyze Diverse Data Types: Effectively manage and analyze diverse data types and structures, ensuring data-driven decision-making and business optimization.
• Implement Data Visualization: Use data visualization tools like Matplotlib, Seaborn, and Plotly to communicate insights and recommendations to stakeholders.
• Apply Machine Learning Techniques: Apply machine learning, statistical modeling, and custom models to solve business problems in areas like supply chain management, pricing, risk assessment, and fraud detection.
• Optimize Business Outcomes: Use optimization tools like IBM CPLEX and Gurobi to identify opportunities for business improvement and implement solutions.

This position can be performed from anywhere in the USA.

Required technical and professional expertise

• Proficiency in Programming Languages: Exposure to programming languages, particularly Python, and experience working with development environments like PyCharm, VS Code, and Jupyter Notebooks.
• Data Manipulation and Analysis: Experience working with data manipulation tools such as Pandas, NumPy, and Dask, and data visualization tools like Matplotlib, Seaborn, and Plotly.
• Database Management: Exposure to managing databases like SQL, MongoDB, Cassandra, PostgreSQL, and MySQL, including scripting abilities in shell scripting.
• Optimization and Statistical Analysis: Experience with optimization tools like IBM CPLEX and Gurobi, and statistical analysis capabilities with tools like SPSS, SAS, R, and Python.
• Version Control and CI/CD: Exposure to version control systems such as Git, GitHub, and GitLab, and continuous integration and deployment (CI/CD) tools like Docker, Podman, and Jenkins.

Preferred technical and professional experience

• Familiarity with Cloud Platforms: Exposure to cloud-based data platforms and technologies, enabling seamless data integration and scalability.
• Knowledge of Big Data Tools: Experience working with big data tools and technologies, such as Hadoop and Spark, to handle large-scale data processing and analysis.
• Understanding of Domain-Specific Models: Exposure to domain-specific models and techniques, such as natural language processing and computer vision, to drive business value in specialized areas.