Full-Time Data Scientist

Position Overview:

As a Data Scientist, you will work closely with cross-functional teams to analyze and interpret large datasets, build predictive models, and provide actionable insights that will help drive business decisions. You will apply statistical methods, machine learning algorithms, and data mining techniques to solve complex problems and contribute to key projects across the organization.

Key Responsibilities:

• Analyze large and complex datasets to uncover patterns, trends, and insights.
• Develop and implement machine learning models, algorithms, and statistical methods to solve business problems.
• Clean, preprocess, and transform raw data into usable formats for analysis.
• Collaborate with stakeholders to define business problems, and deliver data-driven solutions and recommendations.
• Design and build scalable data pipelines for automating data collection and processing.
• Communicate findings clearly and effectively to both technical and non-technical stakeholders through visualizations, reports, and presentations.
• Stay updated on industry trends and new tools/technologies related to data science and machine learning.
• Work with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure, Google Cloud Platform) when necessary.

Required Qualifications:

• Bachelor's or Master s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Strong proficiency in programming languages such as Python, R, or Scala.
• Expertise in data manipulation and analysis libraries (e.g., pandas, NumPy, SciPy).
• Experience with machine learning algorithms and frameworks (e.g., scikit-learn, TensorFlow, Keras).
• Solid understanding of statistics, probability, and hypothesis testing.
• Familiarity with databases (SQL, NoSQL) and big data tools (e.g., Hadoop, Spark).
• Strong data visualization skills using tools like Tableau, Power BI, or matplotlib.
• Experience with cloud platforms (AWS, Google Cloud, Azure) is a plus.
• Excellent problem-solving, analytical, and critical thinking skills.
• Ability to communicate technical concepts to a non-technical audience effectively.
• Preferred Qualifications:
• Ph.D. or advanced degree in Data Science, Machine Learning, Statistics, or a related field.
• Experience in a specific domain (e.g., healthcare, finance, e-commerce) is a plus.
• Familiarity with deep learning techniques and frameworks (e.g., TensorFlow, PyTorch).
• Experience in A/B testing, experimental design, and causal inference.
• Knowledge of data engineering concepts (ETL pipelines, data warehouses, etc.).