• An expert in quantitive analysis, data mining, and the presentation of data beyond the numbers that understands how users interact with both consumer and business products • Able to partner with Product and Engineering teams to solve problems and identify trends and opportunities • Inform, influence, support and execute product decisions and product launches • Critical thinker with strong communication skills • Reliable, self-motivated, shows initiative • Able to work well within a team and independently • Product Operations and designing and evaluating experiments • Monitoring key product metrics, understanding root causes of changes in metrics • Building and analyzing dashboards and reports • Building key data sets to empower operational and exploratory analysis • Evaluating and defining metrics • Exploratory analysis • Proposing what to build in the next roadmap • Understanding ecosystems, user behaviors, and long-term trends • Identifying new levers to help move key metrics • Building models of user behaviors for analysis or to power production systems • Influencing product teams through presentation of data-based recommendations • Communicating state of business experiment results, etc to product teams • Spreading best practices to analytics and product teams • Working in Hadoop and Hive primarily, sometimes MySQL, Oracle, and Vertica • Automating analyses and authoring pipeline via SQL and Python-based framework

• be a primary technical owner of human-in-the-loop augmented recommendations • work in a team that embraces both LLMs and traditional ML • manage experiments to test new features, including communication of results to stakeholders and leadership • solve problems related to recommendation ranking, hyperparameter optimization, and assortment generation • leverage a decade's worth of rich and unique data about clients, merchandise, and interactions • work with product, product design, and engineering teams to create roadmaps for developing new client products, user features, and infrastructure

• Run CRISP-DM projects with a team of 3 data scientists in the following - • Selecting features, building and optimizing classifiers (logistic regression or RF based propensity models) and recommenders using machine learning techniques • Data mining using state-of-the-art methods - Automate scoring using machine learning techniques, build recommendation systems, improve and extend the features used by recommendation and propensity modeling algos • Extending company’s data with third party sources of information when needed • Enhancing data collection procedures to include information that is relevant for building analytic systems • Processing, cleansing, and verifying the integrity of data used for analysis and perform deep EDA (we create our own training data for our models) • Doing ad-hoc analysis and presenting results in a clear manner • Creating recommended and propensity models and tracking of its performance especially to compensate for concept drift

• Onboard a hands-on SME in leveraging big data tech to solve complex data issues. • Spend almost half of time with hands-on coding. • Involves large scale text data processing, event driven data pipelines, in-memory computations, optimization considering CPU core to network IO to disk IO. • Use cloud native services in AWS and GCP.