Mid-Level Data Scientist We are seeking a talented and motivated Mid-Level Data Scientist to join our dynamic team. The ideal candidate will possess a strong foundation in data science methodologies, with a particular emphasis on machine learning, exploratory data analysis, and causal inference. You will be responsible for analyzing complex datasets, developing predictive models, and providing actionable insights to drive business decisions. Key Responsibilities:

• Conduct exploratory data analysis (EDA) to uncover trends, patterns, and anomalies in large datasets.
• Build and implement machine learning models using Python libraries such as Scikit-learn, focusing on clustering and forecasting techniques.
• Apply causal inference methodologies to assess the impact of interventions and provide recommendations based on findings.
• Work closely with cross-functional teams, including data engineers, product managers, and business stakeholders, to understand data needs and deliver insights.
• Maintain clear and organized documentation of data processes, methodologies, and findings to ensure reproducibility and knowledge sharing.
• Write clean, efficient, and well-documented code, adhering to best practices in software development.

Qualifications: Education: Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related field. Experience: 3-5 years of experience in data science or a related field, with a proven track record of successful project delivery. Technical Skills:

• Proficiency in Python and its data science libraries (e.g., Scikit-learn, Pandas, NumPy).
• Strong understanding of machine learning algorithms and techniques, particularly clustering and forecasting.
• Familiarity with causal inference methods and their applications in data analysis.

Analytical Skills: Strong analytical and problem-solving skills, with the ability to interpret complex data and communicate findings effectively. Organizational Skills: Excellent coding practices and organizational skills, ensuring code is maintainable and scalable. Communication: Strong verbal and written communication skills, with the ability to present technical concepts to non-technical stakeholders. Additional Information: Application Analytics.