Senior Data Scientist

Role Overview

• Data Analysis & Modeling: Utilize advanced statistical methods and machine learning techniques to analyze large, complex datasets, uncovering insights and trends that inform business strategies.
• Predictive Analytics: Build predictive models to forecast business outcomes, optimize processes, and drive decision-making across various departments.
• Feature Engineering: Design and implement feature engineering techniques to improve the accuracy and performance of machine learning models.
• Collaboration: Work closely with cross-functional teams (business, product, engineering, etc.) to identify key business problems and translate them into data science solutions.
• Data Preparation: Clean, preprocess, and organize raw data from different sources to ensure it is suitable for analysis and modeling.
• Machine Learning Model Development: Develop and deploy machine learning models for classification, regression, clustering, and recommendation systems.
• Evaluation & Optimization: Evaluate the performance of models using various metrics (e.g., accuracy, precision, recall, F1 score) and optimize them for real-world performance.
• Data Visualization & Reporting: Create clear, insightful visualizations and reports to communicate findings to non-technical stakeholders.
• Automation: Automate repetitive data processing and reporting tasks to increase efficiency and reduce manual effort.
• Research & Innovation: Stay up-to-date with the latest developments in data science, machine learning, and AI, and bring new ideas and techniques to the team.
• Deployment & Monitoring: Implement models into production environments and monitor their performance over time to ensure they meet business requirements.

Requirements

• Education: Bachelor's or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
• Experience: Proven experience as a Data Scientist, with a strong background in machine learning, data analysis, and statistical modeling.
• Programming: Strong programming skills in Python (preferred), R, or similar languages. Experience with libraries like Pandas, NumPy, scikit-learn, TensorFlow, or PyTorch.
• Machine Learning: Expertise in building and deploying machine learning models for classification, regression, time-series forecasting, and NLP tasks.
• Data Processing: Experience with data wrangling, feature engineering, and working with large, unstructured datasets.
• SQL: Strong proficiency in SQL for querying databases and working with structured data.
• Cloud Platforms: Experience with cloud platforms such as AWS, Azure, or Google Cloud for deploying models and managing data pipelines.
• Data Visualization: Proficiency in data visualization tools such as Power BI, Tableau, or programming libraries like Matplotlib, Seaborn, or Plotly.
• Problem Solving: Strong analytical and problem-solving skills, with a passion for applying data science to real-world business challenges.
• Communication: Excellent communication skills to explain complex technical concepts to non-technical stakeholders and collaborate across teams.

Tech Stack

• AWS
• Azure
• Cloud
• Numpy
• Pandas
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tableau
• Tensorflow

Benefits

• Competitive salary and flexible payment methods.
• Opportunities for growth and professional development.
• Flexible working hours and remote work options.
• A collaborative, innovative, and inclusive work environment.
• Be a part of a data-driven culture that values impactful insights and decision-making.