Senior Data Scientist

What you'll work on

• 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.

What we're looking for

• Big Data Tools: Familiarity with big data tools such as Apache Spark, Hadoop, or Databricks.
• Deep Learning: Experience with deep learning techniques and frameworks (e.g., TensorFlow, Keras, PyTorch).
• Natural Language Processing (NLP): Experience with NLP techniques such as sentiment analysis, text classification, or language models.
• Statistical Analysis: Strong foundation in statistical analysis and hypothesis testing.
• Data Engineering: Experience with building and optimizing data pipelines for data collection, processing, and storage.
• Version Control: Familiarity with version control systems like Git.
• Business Acumen: Ability to understand business goals and align data science solutions to meet those objectives.

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.