Top Software Data Science Jobs

Leads data science initiatives using machine learning, statistical modeling, and large-scale data analysis. Partners with software engineers and product managers to develop customer-focused products, builds and validates models across the full development lifecycle, translates technical findings into business outcomes, evaluates emerging technologies, and develops data science talent. Requires substantial experience with programming, machine learning, relational databases, cloud computing, and people management.

Develop Generative AI solutions for financial services using Python, PyTorch, LangChain, AWS, Spark, vector databases, multi-agent systems, and advanced RAG pipelines. Build, fine-tune, evaluate, validate, and deploy machine learning models across large numeric and textual datasets. Collaborate with data scientists, machine learning engineers, and product managers while translating technical findings into business outcomes.

Leads data science and machine learning initiatives for payment-network applications, including fraud, risk, merchant intelligence, and monitoring. Partners with data scientists, engineers, and product managers to build scalable models and AI tools. Responsibilities span model design, training, evaluation, validation, and implementation using Python, AWS, H2O, Spark, and other technologies. The role also requires stakeholder communication, innovation, and team talent development.

Build and support an advocate performance data platform using scalable pipelines, semantic models, governed metrics, and machine learning capabilities. Develop AI-ready data products, integrate operational and coaching data, establish data quality and lineage controls, and enable near-real-time performance insights. Deploy and maintain production ML solutions through MLOps, automation, monitoring, and CI/CD while collaborating with and mentoring technical teams.

Performs advanced analysis and data mining on large structured and unstructured datasets to generate business insights. Develops machine learning algorithms and statistical models to predict outcomes and recommend actions. Conducts analytical experiments for product and process optimization, presents findings through visualization and data storytelling, and collaborates with data architects, analysts, engineers, and visualization experts.

Develop predictive and prescriptive models using structured and unstructured data, with a focus on NLP and GenAI. Responsibilities include exploring datasets, building visualizations, generating business insights, automating data workflows, developing interactive applications, and collaborating with stakeholders across marketing, sales, finance, and supply chain.

Leads advanced data science initiatives for Caterpillar’s Service Events domain. Develops and productionizes machine learning and statistical models using large-scale structured and unstructured data, including NLP and predictive analytics. Directs data acquisition, transformation, modeling, and optimization efforts; defines analytical problems and success criteria with stakeholders; communicates findings through visualization and storytelling; and provides technical leadership, coaching, and mentoring across cross-functional teams.

Analyze large datasets using quantitative analysis, data mining, statistical modeling, metrics, dashboards, and reports to generate actionable product and business insights. Collaborate with product managers, engineers, marketers, and designers to improve product decisions and adoption. Develop scalable data solutions using AI tools while maintaining statistical rigor, and independently drive analytical projects to completion.

Data Science Intern supporting advanced analytics, statistical modeling, process improvement, data discovery, dashboard development, and performance measurement for BAE Systems’ Space & Mission Systems sector. The intern will manipulate data, develop models and metrics, investigate data quality, collaborate with business and technical stakeholders, and present analytical findings. Work is primarily on-site with possible travel between campuses and other locations.

Join Celonis’s 12-month Orbit Program as an Associate Value Engineer. Consult customers, identify business challenges, build AI and machine-learning prototypes, develop SQL and PQL data models, lead proof-of-concept projects, present solutions to executives, quantify ROI, and establish trusted customer relationships. The role combines technical analytics, enterprise software consulting, customer engagement, and value realization, with mentorship and training provided.

Paid, full-time 11-week data science internship supporting accessibility initiatives. The intern will clean and document datasets, analyze census and ACS disability data, map geographic insights, evaluate assistive technology feature usage, build dashboards, compare external and internal data, and present findings and recommendations. The role also includes professional development, mentorship, networking, and local travel to intern events.

Lead the research and implementation of scalable machine learning and data science solutions for customer lifetime value prediction. Apply causal analysis, deep learning, and back-testing to improve model performance, generate actionable insights, and guide business decisions. Translate complex findings for technical and non-technical stakeholders, solve ambiguous business problems, and collaborate across product, engineering, marketing, pricing, and customer service teams.