Brian Musa
About Candidate
Brian is a compassionate, data-driven analytical chemistry graduate with expertise in computing, data analysis, and practical experience in the quality department. Holds a degree in Analytical Chemistry with Computing and an ALX certified Data Analyst. By combining science and tech skills, this makes Brian an outstanding analytical problem solver, leader and a critical thinker. My strengths include compliance monitoring, safety audits, laboratory testing, and documentation in line with NEMA and WHO standards. I am also experienced in implementing corrective and preventive actions (CAPA), root cause analysis, Excel, SQL, Python, Power BI, and continuous improvement initiatives. Adept at supporting cross-functional teams to ensure operational excellence, good decision-making, and regulatory compliance.
Location
Education
Building on Data Science certification, this comprehensive training focused on the end-to-end data lifecycle, utilizing SQL for complex querying and Python (Pandas, NumPy) for cleaning and manipulating high-volume operational datasets. The curriculum emphasized Power BI for developing automated, JSON-themed dashboards and Advanced Excel for sophisticated modeling and reporting, specifically designed to track performance metrics and KPIs. By integrating Machine Learning for predictive insights with core competencies in data visualization and statistical problem-solving, I have developed the technical capacity to automate repetitive tasks, identify process inefficiencies, and drive data-driven continuous improvement initiatives within fast-paced industrial environments.
This degree provided a rigorous foundation in the Lab-to-Laptop workflow, bridging the gap between physical science and modern data intelligence. The curriculum integrated advanced chemical principles including chromatography, spectroscopy, and material analysis with a dedicated focus on computing for scientific data validation and process modeling. I mastered the application of statistical methods to ensure high-precision results within quality control environments, while utilizing computational tools to monitor chemical processes and maintain documentation consistency. This academic background, combined with a Second-Class Upper Division achievement, established the technical discipline required to manage complex variables, perform root-cause analysis, and ensure operational compliance within regulated manufacturing and laboratory settings.
Work & Experience
RLHF & Model Tuning - Evaluated and ranked 150+ complex AI-generated responses weekly across reasoning,
factuality, and domain-specific benchmarks, providing granular feedback that directly improved model response
accuracy by >20%.
Prompt Engineering & Stress-Testing - Authored 200+ targeted edge-case prompts in Python, data analysis, and
domain-specific topics to red-team Large Language Models (LLMs), successfully identifying hallucinations and
improving safety policy compliance to 99%.
Quality Assurance & Verification - Reviewed and audited 500+ text, code, and multimodal outputs, applying strict
task rubrics and guideline criteria to maintain a >98% individual evaluator accuracy score on platform quality
benchmarks.
Code & Data Validation - Executed code execution checks, entity extraction, and source-grounding validations
across Python and SQL datasets, reducing factual inconsistencies in technical outputs by ~35%.
RLHF Justification & Documentation - Drafted concise, structured qualitative justifications analyzing model logic
and safety violations, assisting machine learning teams in accelerating model alignment and fine-tuning cycles by
~25%.
Redesigned the Environment Master Data framework, consolidating 5 fiscal years (F21–F26) of multi-source
datasets covering energy, water, biomass, biodiesel, and effluent into a unified SQL-ready structure - reducing data
retrieval time by ~40%.
Built Python (Pandas) and VBA automation scripts that eliminated manual data entry for monthly energy and
environmental returns, cutting the reporting cycle from 3 days to under 4 hours (~85% reduction) and reducing input
errors to near-zero.
Designed and deployed Power BI dashboards tracking 15+ safety and environmental KPIs (TRIR, Near-Miss
trends, energy intensity), enabling weekly self-service reporting and directly informing site management decisions.
Wrote and maintained SQL queries and Python ETL scripts to extract, clean, and validate data from multiple site
channels, ensuring >98% data accuracy for GRIM submissions to Diageo's global reporting platform.
Documented all data pipelines, process definitions, and analytical methodologies, enabling seamless handover and
a 100% audit-ready compliance posture for regulatory, ISO 14001, and ISO 45001 reviews.
Conducted data assurance audits across environmental datasets, identifying and correcting discrepancies that
improved KPI reliability and reduced reporting rework by approximately 30%.
Conducted 50+ routine tests weekly, including pH, turbidity, conductivity, and COD, to monitor water and wastewater quality. I learned how the tools worked in the practical world.
Ensured accuracy of results by calibrating and validating laboratory equipment against QA protocols, and this improved test reliability.
Wastewater characteristics analyzed to support process optimization and corrective actions (CAPA), contributing to a 10% improvement in treatment efficiency.
Generated and documented test reports for clients using LIMS and digital reporting tools; this supported audit readiness and compliance.
Collaborated with a cross-functional QA team, and this made the distribution and delegation of the work easier, hence facilitating efficiency in teamwork.
