• Support and manage highly complex software development initiatives, including data engineering, data analysis, user support, and enterprise-scale data solutions • Manage and drive integrated teams, delivering high-quality technology solutions across big data platforms • Designing, developing, and maintaining highly scalable ETL applications on Hadoop/Spark environments • Delivering big data projects using Spark, Python, Scala, SQL, and Hive within distributed computing ecosystems • Leading and mentoring onshore and offshore development teams, ensuring high-quality delivery and consistent engineering standards • Actively participating in—and at times leading—Agile/Scrum ceremonies, including daily stand-ups, sprint planning, retrospectives, and code reviews • Tuning and optimizing ETL pipeline performance to meet strict SLAs across highly complex, concurrent processing environments • Engaging with business partners to understand needs, translate requirements, and identify appropriate technology solutions • Coordinating development project execution from initiation through release, including dependency planning and sprint scheduling • Managing team velocity, financials, and other critical delivery metrics • Reviewing product backlog, designs, code, and test results to mitigate delivery risks and ensure product quality • Leading adoption of innovative technologies to support evolving software product and data platform needs • Assessing, installing, and enhancing software required for the organization’s broader big data platform • Building robust ETL pipelines supporting day-to-day client delivery operations

• Complete entry and processing of incoming requests for new creation of master data for material, accounts receivables, accounts payables, conditions and other master data. • Changes to master data and subsequent maintenance of existing master data according to the specifications of the specialist departments. • Mass maintenance of data within the scope of project work, cleaning of incorrect data according to the specifications of the specialist departments. • Control and monitoring task, creation of data extracts according to the specifications of the specialist departments. • Monitoring of the quality of data entries and data changes in the IT Applications. • Monitoring of system-relevant messages in the Application, creation of reports on the data status. • Checking changes for completeness, checking and evaluating the logistical and MRP status of materials affected by change. • Planning and technical implementation of changes through revision or discontinuation, scheduling changes, monitoring discontinuation, careful documentation of change processes. • Complete capture and processing of incoming requests for ERP user entry and exit. • Support the Accounting team and verify business partner bank account information. • Maintain the Sales Price Master data in SAP based on the Key-Account Management requirements. • Support the SCM team to manage the Long term vendor declaration Management. • Maintain strict compliance with the quality, environmental and occupational health and safety regulations and implementation of the instructions of the quality, environmental management and/or occupational health and safety officer.

At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what’s next. The Ads Data Science & Engineering team is responsible for the foundational logic of the Netflix ads business. We develop the analyses, tools, and predictive algorithms that drive member joy and advertiser value. As a Data Scientist in this 0–1 space, you will not just execute tasks; you will be a primary architect of our experimentation roadmap, helping us navigate the transition from a nascent offering to a sophisticated global marketplace. Key Responsibilities - Scalable Experimentation & Frameworks: Design and execute rigorous experimental frameworks. You will lead the transition from manual analysis to automated, scalable solutions integrated with the Netflix Experimentation Platform, defining best practices that ensure trustworthy decision-making at scale. - Marketplace Dynamics & Auction Theory: Drive the advancement and implementation of biddable media models and dynamically priced auctions. You will develop strategies to optimize marketplace mechanics, ensuring balance between supply, demand, and member experience. - Global Expansion & Scaling: Partner with Product and Engineering to solve "cold-start" measurement challenges and rapidly scale our ad platform across diverse international markets. - Strategic Thought Leadership: Act as a high-level consultant to Product, Strategy, and Engineering teams. You will autonomously identify research opportunities, quantify their potential business impact, and advocate for resource allocation to pursue them. - Collaborative Partner: Cultivate strong partnerships with cross-functional stakeholders including product, engineering, operations, design, and consumer research. - Technical Excellence: Deliver end-to-end solutions using advanced causal inference, machine learning, and data exploration, maintaining a high bar for documentation and reproducibility. Requirements - Advanced Quantitative Background: MS or PhD in a quantitative field (e.g., Statistics, Economics, Mathematics) or equivalent practical experience. - Specialized Domain Expertise: Significant experience with auction dynamics, yield management, or supply-demand matching within a marketplace setting. - Statistical Rigor: Mastery of causal inference and experimental design, with specific experience solving for interference and network effects in marketplace environments. - Technical Stack: Expert proficiency in Python or R, and advanced SQL. - Strategic Communication: Ability to translate complex statistical results into actionable business narratives for stakeholders at all levels of the organization. - Netflix Culture: A self-starter who thrives in an environment of radical transparency and high autonomy. You are a mentor and a collaborator who prioritizes the inclusion of diverse perspectives to reach the best possible decisions. Generally, our compensation structure consists solely of an annual salary; we do not have bonuses. You choose each year how much of your compensation you want in salary versus stock options. To determine your personal top of market compensation, we rely on market indicators and consider your specific job family, background, skills, and experience to determine your compensation in the market range. The range for this role is $372,000.00 - $600,000.00. This compensation range will vary based on location. Netflix provides comprehensive benefits including Health Plans, Mental Health support, a 401(k) Retirement Plan with employer match, Stock Option Program, Disability Programs, Health Savings and Flexible Spending Accounts, Family-forming benefits, and Life and Serious Injury Benefits. We also offer paid leave of absence programs. Full-time hourly employees accrue 35 days annually for paid time off to be used for vacation, holidays, and sick paid time off. Full-time salaried employees are immediately entitled to flexible time off. See more details about our Benefits here. Netflix is a unique culture and environment. Learn more here. Inclusion is a Netflix value and we strive to host a meaningful interview experience for all candidates. If you want an accommodation/adjustment for a disability or any other reason during the hiring process, please send a request to your recruiting partner. We are an equal-opportunity employer and celebrate diversity, recognizing that diversity builds stronger teams. We approach diversity and inclusion seriously and thoughtfully. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service. Job is open for no less than 7 days and will be removed when the position is filled.

Overview Do you enjoy solving problems, looking at problems through a different lens, and working closely with customers to innovate new solutions to complex problems? Do you jump with excitement at the opportunity to identify trends and provide unique business solutions? Do you want to join a team where learning about a new technology or solution is part of our work every day? The Industry Solutions Delivery (ISD) Engineering & Architecture Group (EAG) is a global consulting and engineering organization that supports our most complex and leading-edge customer engagements. Driving early-stage deliveries, enhances ISD’s technical capabilities, and partnering with others to develop approaches, innovative solutions, and engineering standards in order to set our sales and delivery teams up for success. Leveraging the principles of model, care, and coach, we provide consistent high-quality customer experience through technical and AI leadership and IP capture centered on delivery truth. Responsibilities ​Business Understanding and Impact - Leverages understanding of data science and business to examine a project and consider factors that can influence final outcomes within a technical area. Evaluates project plan for resources, risks, contingencies, requirements, assumptions, and constraints. Documents key business objectives. Effectively communicates business goals in analytical and technical terms. Consistently shares insights with stakeholders. Data Preparation and Understanding - Understands where to acquire data necessary for successful completion of the project plan. Utilizes querying, visualization, and reporting techniques to describe acquired data, including format, quantity, identities, and other surface properties. Explores data for key attributes and contributes to the development of data quality report describing results of the task, initial findings, and impact on the project. Collaborates with others to perform data-science experiments using established methodologies, statistics, optimization, and probability theory for general purpose software and statistical packages. Assesses different tools and techniques and selects the appropriate one. Serves as an effective partner in data preparation efforts to Solution Architects, Consultants, and Data Engineers. Adheres to Microsoft's privacy policy related to collecting and preparing data. Identifies data integrity problems. Modeling and Statistical Analysis - Leverages knowledge of machine learning solutions (e.g., classification, regression, clustering, forecasting, natural language processing [NLP], image recognition) and individual algorithms (e.g., linear and logistic regression, k-means, gradient boosting, autoregressive integrated moving average [ARIMA], recurrent neutral networks [RNN], long short-term memory [LSTM] networks) to identify the best approach to complete objectives. Understands modeling techniques (e.g., dimensionality reduction, cross-validation, regularization, encoding, assembling, activation functions) and selects the correct approach to prepare data, train and optimize the model, and evaluate the output for statistical and business significance. Understands the risks of data leakage, the bias/variance tradeoff, methodological limitations, etc. Writes all necessary scripts in the appropriate language: T-SQL, U-SQL, KQL, Python, R, etc. Constructs hypotheses, designs controlled experiments, analyzes results using statistical tests, and communicates findings to business stakeholders. Effectively communicates with diverse audiences on data-quality issues and initiatives. Understands operational considerations of model deployment, such as performance, scalability, monitoring, maintenance, integration into engineering production system, and stability. Develops operational models that run at scale through partnership with data engineering teams. Evaluation - Understands linkage between achieved model and business objectives. Assists with testing models on test applications and on real data or production data. Analyzes model performance. Incorporates implicit and explicit customer feedback into model evaluation. Conducts review of data analysis and modeling techniques to determine factors that may have been overlooked or need to be reexamined. Contributes to the summary of the review process. Industry and Research Knowledge/Opportunity Identification - Learns and understands the current state of the industry, including knowledge of tools, techniques, strategies, and processes that can be utilized to improve process efficiency and performance. Maintains knowledge of current trends within the discipline. Attends internal research conferences and participates in on-hands training, when appropriate. Actively contributes to the body of thought leadership and intellectual property (IP) best practices. Coding and Debugging - Understands existing code to write efficient and readable code of their own for a specific feature, seeking guidance as needed. Collaborates with other engineering teams to develop, test, and implement changes to optimize code to improve efficiency, reliability, diagnosability, maintainability, and operability of systems. Develops working expertise in proper debugging techniques such as locating, isolating, and resolving errors and/or defects. Collaborates with other engineers/project team members to integrate data models into customers' engineering systems. Understands big-data software engineering concepts, such as Hadoop Ecosystem, Apache Spark, continuous integration and continuous delivery (CI/CD), Docker, Delta Lake, MLflow, AML, and representational state transfer (REST) application programming interface (API) consumption/development. Business Management - Develops understanding of data structures and their relationship to Microsoft's customer business. Observes senior engineers and learns best practices in identifying growth opportunities, understanding strategy goals, customer- and product-strategy goals, and exploring opportunities for machine learning (ML) application, seeking guidance when needed. Understands business goals of the customer, per engagement basis. Customer/Partner Orientation - Leverages understanding of data science and business to examine projects through a customer-oriented focus. Manages customer expectations regarding project/product progress and timeline. Takes responsibility to enhance customer excellence. Assists and learns from senior team members interpret results, develop insights, and communicate results to customers. Possesses basic understanding about model accuracies' dependency on data quality and able to articulate it in customer discussions. Other Embody our culture and values Qualifications ​Required/Minimum Qualifications - Bachelor's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) - OR master's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) or consulting experience. - OR Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field - OR equivalent experience. Additional or Preferred Qualifications - Bachelor's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) - OR master's degree in data science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) - OR Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) - OR equivalent experience. This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.