Job Description:

• Experience: Typically, a minimum of 7+ years of professional experience in data science or related roles, with a strong emphasis on using SQL, R, Python, Advanced Excel, and statistical analysis techniques.

• SQL: Proficiency in SQL (Structured Query Language) for data manipulation, querying, and analysis. Experience in writing complex SQL queries, optimizing query performance, and working with relational databases.

• R and Python: Strong programming skills in R and Python for data analysis, statistical modeling, and machine learning. Experience in data cleaning, transformation, exploratory data analysis, and visualization using R and Python libraries.

• Advanced Excel: Proficiency in advanced Excel functionalities such as data manipulation, formulas, pivot tables, and data visualization. Experience in using Excel for data analysis, reporting, and presenting insights.

• Statistics: Solid understanding of statistical concepts and techniques, including hypothesis testing, regression analysis, experimental design, and data sampling. Ability to apply statistical methods to analyze and interpret complex datasets.

• Machine Learning: Knowledge and practical experience in applying machine learning algorithms and techniques for data analysis, predictive modeling, and pattern recognition. Familiarity with machine learning libraries and frameworks such as scikit-learn, TensorFlow, or Keras.

• Data Visualization: Proficiency in creating clear and impactful data visualizations using tools such as ggplot2, matplotlib, or Tableau. Ability to present data findings and insights to both technical and non-technical stakeholders.

• Data Cleaning and Transformation: Experience in cleaning and transforming raw data into usable formats for analysis. Familiarity with data cleaning techniques, data wrangling, and handling missing or inconsistent data.

• Data Manipulation and Querying: Proficiency in manipulating and querying large datasets using programming languages and tools such as SQL, R, and Python. Experience in data extraction, transformation, and loading (ETL) processes.

• Statistical Modeling: Strong understanding of statistical modeling techniques, including regression analysis, time series analysis, clustering, and classification. Ability to develop and evaluate statistical models for various business applications.

• Data Mining and Pattern Recognition: Experience in data mining techniques, feature engineering, and pattern recognition to extract insights and discover valuable patterns from complex datasets.

• Communication and Collaboration: Excellent communication skills, both verbal and written, to effectively convey data findings and insights to stakeholders. Ability to collaborate with cross-functional teams and work effectively in a team-oriented environment.

• Domain Knowledge: Familiarity with the specific domain or industry relevant to the role, such as finance, healthcare, marketing, or e-commerce. Understanding of business processes and data requirements in the domain.

• Master's or Ph.D. Degree: A master's or Ph.D. degree in a relevant field such as data science, statistics, mathematics, computer science, or a related discipline is often preferred for senior-level positions.

• Certifications: Relevant certifications in data science, machine learning, or related areas (e.g., SQL certifications, R or Python certifications, statistical modeling certifications) are beneficial but not always required.