Mid-Level Data Analyst

Darum lohnt es sich

At this stage, remove any duplicate, corrupted, or blank records that could distort analysis Exploratory Data Analysis (EDA): perform exploratory analyses to understand data distributions and identify patterns and/or outliers that may indicate initial trends Report and dashboard development: create visual reports and dashboards using Certsys tools and/or client-provided tools that highlight key insights identified during collection, cleaning, and EDA Data modeling: actively participate in designing data models and database structures that support the organization’s analytical needs Statistical analysis: apply basic statistical techniques to understand relationships between variables, perform hypothesis testing, and conduct comparative analyses based on business rules Team collaboration: work with business teams, data scientists, and other professionals to gather analytical requirements and translate them into solutions Understanding business objectives: maintain a solid understanding of the organization’s business goals to ensure data analysis is aligned with company objectives Technical skill development: continuously improve technical skills, including programming languages such as SQL, Python, or R, and the latest data analysis tools Communication of results: effectively communicate data analysis results to technical and non-technical stakeholders, ensuring insights are actionable and can influence business decisions Identify opportunities to improve business processes based on data analysis findings Requirements Bachelor’s degree in an IT-related field (preferred) Intermediate knowledge of Databases and Data Structures (preferred) Basic knowledge of Programming Languages (e.g., Python, R, SQL, Java, Scala) (preferred) Basic knowledge of Business Process Automation (preferred) Basic knowledge of Machine Learning (e.g., regression, decision trees, neural networks, natural language processing - NLP, clustering) (preferred) Intermediate knowledge of Data Analysis (e.g., descriptive statistics, data visualization, exploratory data analysis, predictive modeling, data mining) (preferred) Basic knowledge of Big Data (e.g., Apache Hadoop, Spark, Hive, Pig) (preferred) Knowledge of relational and non-relational databases (preferred) Knowledge of programming languages (e.g., Scala, C++) (preferred) Knowledge of Data Visualization techniques and tools (e.g., Tableau, Power BI) (preferred) Knowledge of Advanced Statistics (e.g., time series models, survival analysis) (preferred) Experience with Agile methodologies and software development practices (preferred) ATS Optimization Keywords Below are skills and terms extracted directly from this job posting to improve Applicant Tracking System (ATS) visibility.

Hard Skills SQL Python R Java Scala Data Analysis Statistical Analysis Data Modeling Machine Learning Big Data Soft Skills Team collaboration Communication Understanding business objectives Technical skill development Certifications & Qualifications Bachelor’s degree in IT-related field #J-18808-Ljbffr Responsibilities Collect data from multiple sources, whether from databases or via data collection tools Data cleaning and preparation: process raw data to ensure it is clean, consistent, and ready for analysis.

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