Data Scientist – BI & Advanced Analytics

<p><strong>About Particle41</strong></p> <p>Particle41 is a software engineering and data consulting firm that partners with ambitious organizations to design, build, and scale modern digital platforms. Our teams work across data engineering, analytics, machine learning, cloud infrastructure, and application development—helping clients turn complex data into actionable outcomes.</p> <p>We believe great decisions come from great data, and we’re building a team of thoughtful, hands-on practitioners who enjoy solving real business problems with analytics and machine learning.</p> <p>⸻</p> <p><strong>Role Summary</strong></p> <p>Particle41 is seeking a Data Scientist with strong analytics fundamentals and practical machine learning experience to support client engagements across multiple industries. In this role, you will work closely with client stakeholders, data engineers, and product teams to transform raw data into insights, predictive models, and decision-support tools.</p> <p>This is a hands-on role—you will be expected to own projects end-to-end, from problem definition and data exploration through modeling, validation, and deployment, often in AWS-based environments.</p> <p>⸻</p> <p><strong>What You’ll Do</strong></p> <p><br>&nbsp; &nbsp; • &nbsp; &nbsp;Develop and implement statistical models and machine learning solutions to solve real-world business problems<br>&nbsp; &nbsp; • &nbsp; &nbsp;Analyze large, messy datasets and apply rigorous data cleaning, feature engineering, and validation techniques<br>&nbsp; &nbsp; • &nbsp; &nbsp;Build predictive and descriptive models to identify trends, patterns, and opportunities<br>&nbsp; &nbsp; • &nbsp; &nbsp;Design and execute experiments (A/B tests, hypothesis testing, model evaluations)<br>&nbsp; &nbsp; • &nbsp; &nbsp;Partner with stakeholders to translate business questions into analytical approaches<br>&nbsp; &nbsp; • &nbsp; &nbsp;Create clear visualizations and narratives that communicate findings to technical and non-technical audiences<br>&nbsp; &nbsp; • &nbsp; &nbsp;Collaborate with data engineers to productionize models and analytics pipelines<br>&nbsp; &nbsp; • &nbsp; &nbsp;Leverage AWS services to build scalable, secure data science solutions</p> <p>⸻</p> <p><strong>Required Qualifications</strong></p> <p><br>&nbsp; &nbsp; • &nbsp; &nbsp;Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field<br>&nbsp; &nbsp; • &nbsp; &nbsp;3+ years of experience in data science, analytics, or applied machine learning roles<br>&nbsp; &nbsp; • &nbsp; &nbsp;Strong programming skills in Python (R acceptable as secondary)<br>&nbsp; &nbsp; • &nbsp; &nbsp;Hands-on experience with machine learning techniques (e.g., regression, classification, clustering, forecasting)<br>&nbsp; &nbsp; • &nbsp; &nbsp;Proficiency in SQL and working with relational and analytical data stores<br>&nbsp; &nbsp; • &nbsp; &nbsp;Experience cleaning and preparing large, complex, or unstructured datasets<br>&nbsp; &nbsp; • &nbsp; &nbsp;Experience using data visualization tools (e.g., Tableau, QuickSight, Power BI, or similar)<br>&nbsp; &nbsp; • &nbsp; &nbsp;Strong analytical thinking, problem-solving skills, and attention to detail</p> <p>⸻</p> <p><strong>Preferred Qualifications</strong><br>&nbsp;<br>&nbsp; &nbsp; • &nbsp; &nbsp;5+ years of applied data science or machine learning experience<br>&nbsp; &nbsp; • &nbsp; &nbsp;Experience deploying or operating models using AWS services (e.g., SageMaker, S3, Athena, Redshift, Lambda)<br>&nbsp; &nbsp; • &nbsp; &nbsp;Experience working in a consulting or client-facing environment<br>&nbsp; &nbsp; • &nbsp; &nbsp;Familiarity with MLOps, model monitoring, or production analytics systems<br>&nbsp; &nbsp; • &nbsp; &nbsp;Experience working with large-scale, multi-source datasets</p> <p>⸻</p> <p><strong>What Success Looks Like in This Role</strong></p> <p><br>&nbsp; &nbsp; • &nbsp; &nbsp;You can clearly explain how you approach messy data and make it analysis-ready<br>&nbsp; &nbsp; • &nbsp; &nbsp;You’ve delivered end-to-end machine learning projects, not just notebooks<br>&nbsp; &nbsp; • &nbsp; &nbsp;You can articulate why a model or approach was chosen, not just how it was built<br>&nbsp; &nbsp; • &nbsp; &nbsp;Your visualizations help leaders make better decisions, not just view metrics<br>&nbsp; &nbsp; • &nbsp; &nbsp;You’re comfortable balancing technical rigor with business pragmatism</p> <p>&nbsp;</p>