Trabajos marketing finance en Hayes

Job Description: As a data analyst, your role involves collecting, processing, and analyzing data to help your organization make informed decisions. Here are the primary tasks and responsibilities associated with a data analyst position: TASKS Gathers, cleans and collates datasets and develops data management processes and policies. Analyses data to identify trends and patterns in a variety of fields, such as opinion polling, predicting demand for goods and services, or the testing of new medications. Creates visual representations of data, such as data dashboards and graphs. Presents findings for technical or non-technical audiences to inform the decisions of companies, government or other organisations. Data Gathering: Identify and collect data from various sources, such as databases, spreadsheets, APIs, and external data providers. Data Extraction: Use tools and techniques to extract relevant data from structured and unstructured sources. Exploratory Data Analysis (EDA): Perform initial investigations on data sets to discover patterns, anomalies, and relationships using statistical tools and visualization techniques. Statistical Analysis: Apply statistical methods to interpret data, including regression analysis, hypothesis testing, and variance analysis. Trend Analysis: Identify trends and patterns in data over time to make predictions or inform business strategies. Skills required for this job role (To excel as a data analyst, you need a blend of technical, analytical, and interpersonal skills. Here are the key skills required for this role:) 1. Technical Skills: Data Analysis Tools: Proficiency in tools such as SQL, Python, R, and Excel for data manipulation and analysis. Database Management: Experience with relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB). Data Visualization: Proficiency in visualization tools such as Tableau, Power BI, and Excel to create clear and informative reports and dashboards. Statistical Analysis: Knowledge of statistical methods and tools to analyze data and draw meaningful conclusions. Programming Skills: Ability to write scripts and automate data processing tasks using programming languages like Python or R. 2. Analytical Skills: Critical Thinking: Ability to approach problems logically and analytically to uncover insights and solutions. Detail-Oriented: High attention to detail to ensure accuracy and reliability in data analysis. Problem-Solving: Strong problem-solving skills to identify issues, analyze data, and develop actionable solutions. Data Interpretation: Ability to interpret complex data sets and translate them into actionable insights and recommendations. 3. Business and Domain Knowledge: Industry Knowledge: Understanding of the specific industry or domain you are working in to contextualize data insights. Business Acumen: Ability to understand business processes and objectives to align data analysis with organizational goals. 4. Interpersonal Skills: Communication: Strong written and verbal communication skills to present findings and insights clearly to both technical and non-technical stakeholders. Collaboration: Ability to work effectively with cross-functional teams, including marketing, finance, operations, and IT. Stakeholder Management: Skills in managing stakeholder expectations and communicating the value of data-driven insights. 5. Project Management: Time Management: Ability to manage multiple tasks and projects efficiently, prioritizing work to meet deadlines. Organizational Skills: Strong organizational skills to handle large volumes of data and maintain accurate records of analyses. 6. Continuous Learning: Adaptability: Willingness to stay updated with the latest data analysis techniques, tools, and industry trends. Curiosity: A natural curiosity and eagerness to learn and explore new data sources and methods. 7. Specialized Skills (Optional but Beneficial): Machine Learning: Understanding of machine learning algorithms and techniques for predictive modeling and advanced data analysis. Big Data Technologies: Familiarity with big data technologies such as Hadoop, Spark, and distributed computing frameworks. Advanced Statistical Methods: Knowledge of advanced statistical methods and their applications in data analysis. 8. Technical Skills: SQL, Python, R, Excel Tableau, Power BI MySQL, PostgreSQL, MongoDB 2. Analytical Skills: Critical thinking and problem-solving Data interpretation and detail-oriented Business Knowledge: Industry-specific knowledge Business acumen Interpersonal Skills: Strong communication and collaboration Stakeholder management Project Management: Effective time management Organizational skills Continuous Learning: Adaptability and curiosity Specialized Skills (Optional): Machine learning knowledge Big data technologies Advanced statistical methods