Data Analyst

We are seeking a skilled Data Analyst to join our team, focusing on the analysis of water heater performance using spreadsheets and other analytical tools. The successful candidate will leverage data on key performance variables—time to heat, energy efficiency, steam quality, temperature accuracy and precision, and scale elimination—to drive improvements in product design, optimize performance, and enhance customer satisfaction. This role requires proficiency in Python, SQL, C++, R or other data segmentation tools to organize, analyze, and visualize data, providing actionable insights to engineering and product teams. 

Key Responsibilities 

• Data Collection and Organization: 

• Gather and organize performance data for water heaters, including time to heat, energy consumption, steam quality metrics, temperature accuracy/precision, and scale buildup metrics. 

• Maintain structured datasets in spreadsheets, ensuring data accuracy and consistency for analysis. 

• Analyze the system to determine signal data/flags which identify key data points  

• Performance Analysis: 

• Analyze time to heat (e.g., seconds to reach target temperature) and correlate with energy input (e.g., kW or kWh) to assess efficiency. 

• Evaluate energy efficiency metrics, such as energy consumption per unit of water heated or steam produced, using calculations derived from raw data. 

• Assess steam quality (e.g., dryness fraction, superheat degree) by analyzing temperature and pressure data to ensure optimal performance. 

• Measure temperature accuracy (how close measurements are to target) and precision (consistency of measurements) using statistical methods. 

• Investigate scale elimination effectiveness by analyzing maintenance records, and performance degradation over time. 

• Spreadsheet Development and Utilization: 

• Design and maintain complex spreadsheets to perform calculations, such as heat transfer rates, energy efficiency ratios, and statistical measures (e.g., mean, standard deviation) for temperature precision. 

• Create pivot tables, charts, and dashboards in spreadsheets to visualize trends, correlations, and performance metrics (e.g., energy efficiency vs. flow rate, scale buildup vs. time). 

• Automate repetitive data processing tasks using spreadsheet functions, macros, or scripts (e.g., Python, C++, Excel , etc.) to improve efficiency. 

• Reporting and Insights: 

• Generate clear, concise reports summarizing findings on water heater performance, highlighting areas for improvement in efficiency, steam quality, or scale prevention. 

• Present data-driven insights to engineering teams to inform design modifications. 

• Collaborate with engineering to validate temperature accuracy and precision against industry standards/customer requirements. 

• Continuous Improvement: 

• Identify trends or anomalies in performance data (e.g., increased time to heat due to scale buildup) and recommend corrective actions. 

• Propose enhancements to data collection methods or testing protocols to improve the reliability of performance metrics. 

• Stay updated on industry benchmarks for water heater performance and integrate them into analysis frameworks. 

Qualifications 

• Education: Bachelor’s degree in Data Analytics, Statistics, Engineering, Physics, or a related field. Advanced degrees or certifications in data analysis are a plus. 

• Experience: 

• 2+ years of experience in data analysis, preferably in a technical or engineering context. 

• Proven expertise in using data analysis (Python, C++, R, Microsoft Excel) including advanced functions (e.g., VLOOKUP, INDEX-MATCH, pivot tables, statistical functions). 

• Experience with water heater performance metrics or thermal systems analysis is a plus. 

• Technical Skills: 

• Advanced proficiency in spreadsheet tools for data manipulation, visualization, and reporting. 

• Familiarity with statistical analysis techniques (e.g., regression, standard deviation) to assess temperature precision and energy efficiency. 

• Basic programming skills (e.g., Excel VBA, Python, or R) for automating spreadsheet tasks or handling large datasets are preferred. 

• Knowledge of thermodynamics, heat transfer, or water chemistry is advantageous but not required. 

• Soft Skills: 

• Strong analytical and problem-solving skills with attention to detail. 

• Ability to translate complex data into actionable insights for non-technical stakeholders. 

• Excellent communication skills for presenting findings to cross-functional teams. 

• Team player with the ability to collaborate with engineers, product managers, and quality assurance teams. 

Key Performance Variables for Analysis 

The Data Analyst will focus on the following variables to evaluate water heater performance: 

• Time to Heat: Time (seconds or minutes) required to heat water from initial temperature (e.g., 5°C) to target temperature (e.g., 100°C for steam). 

• Energy Efficiency: Energy consumption (kW or kWh) per unit of water heated or steam produced, compared against industry standards. 

• Steam Quality: Dryness fraction or degree of superheat, derived from temperature and pressure measurements. 

• Temperature Accuracy and Precision: Accuracy (deviation from target temperature) and precision (consistency of temperature measurements), calculated using statistical methods. 

• Scale Elimination: Effectiveness of scale prevention mechanisms, measured by reductions in scale buildup or maintenance frequency. 

Example Tasks 

• Write a script that imports multiple excel files, extracts key metrics, and writes aggregate data to an excel file.  

• Write a script that collects data from multiple excel files and visualizes aggregate trends.  

• Build a spreadsheet to calculate power requirements (e.g., 7.96 kW for 3 mL/s flow rate from 5°C to steam at 100°C) and compare across different flow rates (e.g., 10 mL/s, 15 mL/s). 

• Create charts to visualize energy efficiency trends across various water heater models. 

• Analyze temperature data to compute standard deviation as a measure of precision, identifying models with inconsistent performance. 

• Develop a dashboard to track scale buildup over time, correlating with water hardness and anti-scale treatments.