Data Scientist

Introduction

The Data Scientist will be responsible for collecting, cleaning, and analyzing structured and unstructured data from multiple sources. They will build, deploy, and maintain predictive and statistical models, develop dashboards and reporting tools, and work with engineering teams to implement scalable data pipelines and machine-learning solutions.

Responsibilities

• Collect, clean, and analyze structured and unstructured data from multiple sources.
• Build, deploy, and maintain predictive and statistical models (e.g., regression, classification, clustering, time-series).
• Develop dashboards and reporting tools to communicate insights to stakeholders.
• Work with engineering teams to implement scalable data pipelines and machine-learning solutions.
• Conduct exploratory data analyses to identify trends, patterns, and opportunities.
• Define and track performance metrics to evaluate model and business impact.
• Present findings clearly to technical and non-technical audiences.
• Stay updated with the latest advances in machine learning, AI, and data science.

Required Qualifications

• Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Strong proficiency with Python or R.
• Experience with data analysis libraries (e.g., Pandas, NumPy), machine-learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch), and SQL.
• Solid understanding of statistical analysis, modeling techniques, and experimental design.
• Experience working with large datasets and cloud platforms (AWS, Azure, Google Cloud Platform).
• Excellent problem-solving skills and critical thinking.

Preferred Qualifications

• Experience with big-data tools (Spark, Hadoop).
• Knowledge of MLOps (Docker, Kubernetes, MLflow, CI/CD for ML).
• Familiarity with data visualization tools such as Power BI, Tableau, Looker, or Plotly.
• Experience working in Agile teams.
• Domain expertise (finance, healthcare, e-commerce, etc.) is a plus.