JOB DESCRIPTION:

We are seeking a highly analytical and detail-oriented Data Science Analyst to join our team. The ideal candidate will have strong skills in data analysis, statistical modeling, and machine learning, with the ability to turn complex data into actionable insights. As a Data Science Analyst, you will work with large datasets to identify trends, build predictive models, and support business decision-making processes.

Responsibilities:

• Collect, clean, and analyze large datasets to uncover insights and trends.
• Develop statistical models and algorithms to predict future outcomes and identify key business opportunities.
• Perform exploratory data analysis (EDA) to understand data patterns and relationships.
• Collaborate with business stakeholders to understand requirements and translate them into data-driven solutions.
• Use data visualization tools to present insights in an easy-to-understand format for non-technical stakeholders.
• Develop and maintain dashboards and reports to track key business metrics.
• Apply machine learning techniques to solve business problems and improve business processes.
• Conduct hypothesis testing and A/B testing to measure the effectiveness of business strategies.
• Work with other teams (e.g., marketing, finance, product) to identify data-driven opportunities for improvement.
• Automate data pipelines and reporting processes to ensure efficiency and scalability.
• Stay up to date with the latest trends and tools in data science and machine learning.

Requirements:

• Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, Mathematics, or a related field.
• Proven experience as a Data Analyst, Data Scientist, or similar role, with a strong focus on data analysis and statistical modeling.
• Proficiency in programming languages such as Python or R.
• Hands-on experience with data visualization tools like Tableau, Power BI, or similar.
• Strong knowledge of SQL for querying and managing relational databases.
• Experience with machine learning algorithms and libraries (e.g., scikit-learn, TensorFlow, PyTorch).
• Solid understanding of statistical analysis and hypothesis testing.
• Familiarity with big data technologies (e.g., Hadoop, Spark) is a plus.
• Strong problem-solving skills and attention to detail.
• Ability to work with complex data sets and extract meaningful insights.
• Excellent communication skills with the ability to explain technical concepts to non-technical audiences.

Preferred Skills:

• Experience with cloud platforms (AWS, Azure, GCP) for data storage and analysis.
• Familiarity with data wrangling and cleaning techniques.
• Knowledge of deep learning techniques and neural networks.
• Experience with data pipelines and automation using tools like Apache Airflow.
• Experience with time-series analysis and forecasting models.
• Familiarity with version control systems (e.g., Git).

Job Type: Full-time
Pay: $70,019.00 - $120,171.00 per year

Schedule:

• Monday to Friday

Experience:

• Machine learning: 3 years (Required)
• Data science: 3 years (Required)
• Data analysis skills: 3 years (Required)

Ability to Commute:

• California, PA 15419 (Required)

Willingness to travel:

• 100% (Required)

Work Location: In perso

• Collect, clean, and analyze large datasets to uncover insights and trends.
• Develop statistical models and algorithms to predict future outcomes and identify key business opportunities.
• Perform exploratory data analysis (EDA) to understand data patterns and relationships.
• Collaborate with business stakeholders to understand requirements and translate them into data-driven solutions.
• Use data visualization tools to present insights in an easy-to-understand format for non-technical stakeholders.
• Develop and maintain dashboards and reports to track key business metrics.
• Apply machine learning techniques to solve business problems and improve business processes.
• Conduct hypothesis testing and A/B testing to measure the effectiveness of business strategies.
• Work with other teams (e.g., marketing, finance, product) to identify data-driven opportunities for improvement.
• Automate data pipelines and reporting processes to ensure efficiency and scalability.
• Stay up to date with the latest trends and tools in data science and machine learning