• BY Solver

Role Description As Lumen continues this journey, Corporate Finance is playing a critical role in shaping the future through data and AI. We are seeking a Senior Data Scientist who is excited by the opportunity to turn information into impact. In this role, you’ll leverage Databricks, Generative AI, and advanced analytics to create scalable solutions that empower smarter decision-making. You’ll help expand self-service insights, automate finance processes, and uncover opportunities hidden within complex datasets. More than building models, you’ll help write the next chapter of Lumen’s transformation—where data becomes a strategic advantage and innovation drives meaningful business outcomes. This is a work from home position from within the US. Main Responsibilities - Build and enhance Databricks-based AI and analytics solutions that improve how Finance teams access and use insights. - Develop scalable data pipelines, workflows, and reporting solutions using Databricks, Data Factory, SQL, Python, and Power BI. - Apply statistical, forecasting, and machine learning techniques to solve business problems and strengthen decision support. - Automate manual processes, improve data quality, and strengthen the performance and reliability of analytics applications. - Partner across Finance, FP&A, Data Science, and IT to turn business needs into practical solutions and actionable insights. - Contribute to high-impact initiatives that advance how data and AI are used across Finance. Qualifications - 3–5 years of experience in data science, data engineering, analytics, or a related role - Strong Python and SQL skills - Experience with Databricks, PySpark, or Spark-based processing - Experience building data pipelines and working with large, complex datasets - Strong analytical, problem-solving, and communication skills Preferred Qualifications - Experience with forecasting, predictive modeling, statistical analysis, or machine learning techniques - Experience with Generative AI or LLM-based applications - Experience with Power BI or similar visualization tools - Exposure to financial data, FP&A, or enterprise reporting Compensation This information reflects the anticipated base salary range for this position based on current national data. Minimums and maximums may vary based on location. Individual pay is based on skills, experience and other relevant factors. - $84,629 - $112,838 in these states: AL, AR, AZ, FL, GA, IA, ID, IN, KS, KY, LA, ME, MO, MS, MT, ND, NE, NM, OH, OK, PA, SC, SD, TN, UT, VT, WI, WV, WY - $88,860 - $118,480 in these states: CO, HI, MI, MN, NC, NH, NV, OR, RI - $93,092 - $124,122 in these states: AK, CA, CT, DC, DE, IL, MA, MD, NJ, NY, TX, VA, WA Benefits - Comprehensive package featuring a broad range of Health, Life, Voluntary Lifestyle benefits and other perks that enhance your physical, mental, emotional and financial wellbeing.

Role Description At QuintoAndar Group, we are committed to promoting gender diversity across our teams. We want to see more women in strategic positions within our technical teams, and we believe we can achieve this through intentional opportunities and actions. This is part of a larger plan to strengthen an equitable culture across multiple aspects. In our Data Science team, you'll have the chance to work as a part of our product squads delivering features that enrich our product and a first-class experience that we are known for. You'll work alongside brilliant minds, with different skills: Product Managers, Designers, UX Writers, Data Scientists, Machine Learning Engineers, Software Engineers, etc. Be prepared for the job of your life - hard challenges, high expectations, intelligent life at work, meaningful conversations, outstanding productivity. Some of the specific problems we work in the Data Science team include: - Search and recommendation systems - Customer experience enhancement using ML such as in automatic routing of tickets - Pricing, liquidity and credit models - Building a platform that enables us to develop and deploy all those solutions Note: This is a senior position. We expect you to have at least 3+ years of experience applying data science and creating machine learning models. If you have the skills, but are starting your journey in Data Science, please apply to the Data Scientist job instead. Qualifications - Expert knowledge of machine learning concepts: regression and classification, clustering, neural networks, feature selection, cross-validation, curse of dimensionality, bias-variance tradeoff, model explainability, etc. - Good understanding of the engineering challenges to deploy machine learning systems to production - Proficiency in Python - Some knowledge or experience with Deep Learning - Experience with technical advice for other data scientists (technical leadership) - Excellent written and verbal technical communication skills - Good English skills (verbal and written) is mandatory Requirements - People that are seeking to learn and deliver real impact through Data Science - Have a MSc. (or Undergrad + intense experience) in machine learning, data science, information retrieval, ranking systems, recommender systems, natural language processing or other relevant fields - Have experience with applied generative AI: AI agents, prompt engineering, LLMs finetuning (lora, qlora, peft), LLM routing, LLM monitoring, LLM guardrails - Have experience working at fast-growing startups Benefits - Competitive salary - Profit sharing - Meal allowance - Health insurance - Dental plan - Life insurance - Childcare subsidy and Atypical Parenthood subsidy - Wellhub - Home office allowance - Employee assistance program (mental health, social, legal, and financial support) - Extended parental leave - Day off on birthday, Mother’s Day, and Father’s Day - Benefits Club (discounts on everyday services) - Discounts at educational institutions - Reading kit for children – PlayKids

• Contribute directly to the technical execution of the data team’s deliverables • Refine and prioritize the data roadmap • Align metrics and definitions with domain owners across the business • Act as the technical interface between the data team and Engineering • Continuously reevaluate the stack, architecture, and use of AI • Ensure data governance • Lead team management (performance reviews, hiring, promotions) • Provide leadership visibility into objectives, KPIs, and deliverables