Data scientist

Roles & responsibilities :

Delivery of Key Projects:

Successfully deliver advanced analytics and data science projects within predefined timelines and budget constraints.

Collaboration:

Collaborate effectively with data engineers and ML engineers to comprehend data and models, utilizing various advanced analytics capabilities.

Solution Development:

Develop efficient solutions for complex problems, showcasing expertise in data structures like Delta/Parquet, databases, data analytics, Python programming, and statistics.

End-to-End Pipeline Management:

Manage the entire data science pipeline, including problem scoping, data gathering, modeling, insights generation, visualizations, monitoring, and maintenance.

Dashboard Design:

Design dashboards using Python Dash (plotly) or Flask framework for effective data visualization and reporting.

Technological Expertise:

Utilize CI/CD tools, data pipeline technologies, and visualization/data storytelling tools proficiently.

ETL Pipeline Building:

Construct ETL pipelines using Python or Azure Synapse to facilitate efficient data processing and management.

Data Procurement and Management:

Independently procure and process data, ensuring seamless integration within the company's overall data management strategy.

Technology Exploration:

Continuously monitor advancements in artificial intelligence technology and identify applications for problem-solving within the company.

Technical Skills:

Proficiency in Python and common libraries (Dash, Pandas, Scikit-Learn, Pydantic, Tensor Flow). Strong knowledge of statistics and Probability. Set up cloud alerts, monitors, dashboards, and logging systems; troubleshoot data platform infrastructure as needed. Experience in Open Shift/Kubernetes and Azure ML is desirable. Proficiency in programming languages: Python (Dash) and database query languages SQL or KQL. Good applied statistical skills, including hands-on experience in time series modeling, predictive modeling, distributions, and regression. Knowledge of Dev Ops/MLOps. Exceptional analytical and problem-solving skills.

Preferred candidate profile

Bachelor's or Master's degree in Computer Science or a related technical field, or equivalent experience. Total of 4 to 8 years of professional experience. Minimum of 4 years of experience in data science/machine learning. Minimum 3 years of experience with SQL. Experience in Dev Ops, preferably with hands-on experience in one or more cloud service providers, with Azure preferred.