{"id":126201,"url":"https://alion.io/job/fis-data-scientist","title":"Data Scientist","company":{"id":4808,"name":"FIS","domain":"fisglobal.com","url":"https://alion.io/company/fis","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":85,"open_postings":72,"ghost_share":0,"stale_share":0.556,"repost_share":0.056,"time_to_fill_p50_days":26,"computed_at":"2026-10-28T05:59:10Z"}},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":75000,"max_usd":161000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":376},"experience_years_min":1,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Machine Learning","optional":false},{"name":"Matplotlib","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Plotly","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Seaborn","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"AWS","optional":true},{"name":"Databricks","optional":true},{"name":"MLFlow","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-08-26T00:00:00Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-28T05:51:44Z","board_verified":true,"closed_at":null,"days_open":63,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":63},"description":"Job Description\nAre you curious, motivated, and forward-thinking? At FIS you’ll have the opportunity to work on some of the most challenging and relevant issues in financial services and technology. Our talented people empower us, and we believe in being part of a team that is open, collaborative, entrepreneurial, passionate and above all fun.\nAbout the role:\nAs a key member of the Data Science team, the data scientist will deploy data-driven exploratory analysis as well as predictive models and AI solutions to solve business problems across the financial services industry, particularly in Risk, Fraud, Marketing, and Portfolio Management. Following the machine learning lifecycle, the data scientist should be able to convert the results into actionable product recommendations to present internally and externally.\nWhat you'll be doing:\nParticipate in the design, development, validation, deployment, and monitoring of advanced analytics, machine learning, and AI solutions that drive measurable business outcomes.\nLeverage expertise in data structures and algorithms to analyze and prepare data for modeling, assembling datasets from both standard and novel data sources and incorporate them into end-to-end analytical solutions.\nApply advanced machine learning, predictive analytics, natural language processing (NLP), and emerging AI techniques (GenAI, Agentic etc.) to solve complex business problems across the payments and financial services ecosystem.\nDesign and execute experiments, hypothesis testing frameworks, and statistical analyses to evaluate business strategies, product enhancements, and operational improvements.\nEstablish and promote best practices in data science, machine learning, feature engineering, experimentation, model governance, and MLOps throughout the organization.\nStay current on industry trends in machine learning, AI, Generative AI, and financial services analytics; bring relevant innovations to the team.\nWhat you bring:\nMaster's degree or higher in Mathematics, Computer Science, Engineering, Operations Research, Statistics, or a related quantitative discipline.\n1-3 years of experience developing and deploying end-to-end machine learning, predictive analytics, and data science solutions within the Payments, Banking, or Financial Services industry.\nStrong proficiency in Python and SQL; experience with big data technologies such as Spark, PySpark, a plus.\nHands-on experience with data wrangling, feature engineering, and model development using libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, or Plotly.\nDemonstrated experience building and deploying machine learning models in a production or near-production environment.\nProficiency with data visualization and business intelligence tools (e.g., Tableau or equivalent).\nStrong analytical thinking and problem-solving skills; ability to translate ambiguous business problems into rigorous analytical frameworks.\nAbility to work collaboratively across product, engineering, and business teams.\nNice to have:\nExperience within the Payments, Banking, or Financial Services industry.\nHands-on experience with the Databricks platform, including MLflow, Model Registry, collaborative notebooks, and MLOps workflows.\nExperience deploying cloud-native machine learning solutions, particularly within AWS environments.\nFamiliarity with emerging advancements in Transformer Models and Agentic AI technologies.\nKnowledge of model governance, regulatory compliance, and MLOps best practices within regulated financial services environments.\nWhat we offer you:\nA career at FIS is more than just a job. It’s the chance to shape the future of fintech. At FIS, we offer you:\nA voice in the future of fintech\nAlways-on learning and development\nCollaborative work environment\nOpportunities to give back\nCompetitive salary and benefits\nPrivacy Statement\nFIS is committed to protecting the privacy and security of all personal information that we process in order to provide services to our clients. For specific information on how FIS protects personal information online, please see the Online Privacy Notice.\nEEOC Statement\nFIS is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here supplement document available here\nFor positions located in the US, the following conditions apply. If you are made a conditional offer of employment, you will be required to undergo a drug test. ADA Disclaimer: In developing this job description care was taken to include all competencies needed to successfully perform in this position. However, for Americans with Disabilities Act (ADA) purposes, the essential functions of the job may or may not have been described for purposes of ADA reasonable accommodation. All reasonable accommodation requests will be reviewed and evaluated on a case-by-case basis.\nSourcing Model\nRecruitment at FIS works primarily on a direct sourcing model; a relatively small portion of our hiring is through recruitment agencies. 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