Job Description
As a Data Scientist Intern, you will be responsible for analyzing complex datasets to extract valuable insights, develop predictive models, and support data-driven decision-making across various departments. You will collaborate with cross-functional teams to understand business needs and deliver actionable recommendations. This role requires a strong analytical mindset, proficiency in statistical methods, and experience with data science tools and techniques.
Key Responsibilities:
• Collect, clean, and preprocess large datasets from various sources to ensure data quality and integrity.
• Conduct exploratory data analysis (EDA) to identify patterns, trends, and anomalies in the data.
• Develop and implement machine learning models and algorithms for predictive and prescriptive analytics.
• Collaborate with stakeholders to define business problems and translate them into data science projects.
• Perform hypothesis testing, A/B testing, and other statistical analyses to support business decisions.
• Create data visualizations and dashboards to communicate findings and insights to non-technical stakeholders.
• Stay up-to-date with the latest advancements in data science, machine learning, and artificial intelligence.
• Document methodologies, processes, and findings to ensure reproducibility and knowledge sharing.
• Work closely with other teams at SRG to ensure data outputs exceed our client needs.
Qualifications:
• Bachelor's or Master's degree in Data Science, Statistics, Computer Science, Mathematics, or a related field.
• Strong programming skills in Python or R.
• Knowledge of data manipulation and analysis using libraries like pandas, numpy, and scikit-learn.
• Understanding of basic machine learning algorithms and techniques (e.g., regression, classification, clustering).
• Familiarity with data visualization tools such as Tableau, Power BI, or Matplotlib.
• Basic understanding of SQL and database management systems.
• Excellent problem-solving skills and attention to detail.
• Strong communication skills, with the ability to explain complex concepts to non-technical audiences.
• Passion for data science and eagerness to learn and grow in the field.
• Internship or academic project experience in data science or a related field is a plus but not required.
• Familiarity with natural language processing (NLP) and deep learning frameworks (e.g., TensorFlow, PyTorch) is a plus.
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