Data Analysis: Analyze large, complex datasets to extract valuable insights and patterns that drive business decisions and strategies.
Predictive Modeling: Develop and deploy machine learning models for predictive analytics, including customer segmentation, churn prediction, and demand forecasting.
Data Visualization: Create visually compelling and informative data visualizations and dashboards to communicate insights and findings to stakeholders.
Statistical Analysis: Apply statistical techniques and methods to analyze data and test hypotheses, ensuring robustness and reliability of results.
Feature Engineering: Engineer features from raw data to enhance model performance and accuracy, leveraging domain knowledge and creativity.
Model Evaluation and Optimization: Evaluate model performance using appropriate metrics and techniques, and iteratively optimize models for better accuracy and generalization.
Collaboration: Collaborate closely with cross-functional teams, including data engineers, software developers, and business stakeholders, to understand business requirements and deliver actionable insights.
Experimentation and A/B Testing: Design and execute controlled experiments and A/B tests to measure the impact of product changes and marketing initiatives.
Continuous Learning: Stay updated with the latest developments in data science, machine learning, and related fields, and apply new techniques and methodologies to solve business problems.

Requirements:

Master's degree or PhD in Computer Science, Statistics, Mathematics, or related field.
3+ years of experience in data science or related roles, with a strong focus on machine learning and statistical analysis.
Proficiency in programming languages such as Python or R, and libraries such as scikit-learn, TensorFlow, or PyTorch.
Experience with data manipulation and analysis using SQL and relational databases.
Strong understanding of machine learning algorithms, techniques, and methodologies, with hands-on experience in model development and deployment.
Expertise in data visualization tools and techniques (e.g., Matplotlib, Seaborn, Tableau) to communicate insights effectively.
Excellent problem-solving skills and ability to translate business requirements into data-driven solutions.
Strong communication and interpersonal skills, with the ability to collaborate effectively with cross-functional teams.
Proactive attitude towards learning and professional development, with a passion for exploring new techniques and approaches in data science.

Preferred:

Experience with big data technologies and frameworks (e.g., Hadoop, Spark).
Knowledge of natural language processing (NLP) and text analytics techniques.
Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and associated services (e.g., AWS SageMaker, Azure Machine Learning).
Certifications in data science or related fields (e.g., Google Professional Data Engineer, AWS Certified Machine Learning - Specialty)

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