Position: Senior Data Scientist (500)
Company: PSR
Experience required: 5y
Category: Data Science
English level: intermediate
Published: 2022-04-01T00:00:00+03:00

It is a subscription billing and revenue management platform powering some of the fastest-growing brands around the world today, including Calendly, Hopin, Pret-a-Manger, Freshworks, Okta, Study.com, and others. Thousands of SaaS and subscription-first businesses process over billions of dollars in revenue every year through the platform. Headquartered in San Francisco, CA, our 500+ team members work remotely throughout the world, including India, the Netherlands, Paris, Spain, Australia, and the US. The Company has raised over $230 million in capital and is funded by Accel, Tiger Global, Insight Partners, Steadview Capital, and Sapphire Ventures. We’re on a mission to push the boundaries of subscription revenue operations - not just ours, but every customer and prospective business with a recurring revenue model.

**Roles & Responsibilities:**
1. Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
2. Develop a use case roadmap for a problem area or capability for the business. Frame the business problem into a Data Science or modeling problem.
3. Extract data from multiple sources. Mine and analyze data from company databases to drive optimization and improvement of products.
4. Work as the data strategist, identifying and integrating new datasets that can be leveraged through our product capabilities and working closely with the engineering team to strategize and execute the development of data products.
5. Enhance data collection procedures to include information that is relevant for building analytic systems. Processing, cleansing, and verifying the integrity of data used for analysis. Undertake to preprocess structured and unstructured data.
6. Run data exploration to understand relationships and patterns within the data, develop data visualization to represent, and be able to demonstrate the relationships identified from data exploration.
7. Data mining using state-of-the-art methods. Selecting features, building and optimizing classifiers using machine learning techniques.
8. Refine and deepen understanding of the algorithmic and inferential aspects of statistical analysis. Evaluate new algorithms from the latest research and develop intuition about the problems for which they are likely to improve the state of the practice.
9. Build training pipelines for the production environment. Develop and execute a plan for continuous iteration and refinement of a new model.
10. Provide inputs for design, quality assurance parameters, and support implementation for the model in an online environment.
11. Provide inputs and determine infra requirements and infra management for model deployment.
12. Lead debugging of data pipelines and model behavior in the production
environment. Develop dashboards to enable easy tracking and communication of model impact.

**Desired Skills & Experience:**
1. We’re looking for someone with 5-7 years of experience manipulating data sets and building statistical models, with a Bachelor’s/Master’s/PhD degree in Statistics, Mathematics, Computer Science, or another quantitative field, from any of the top-tier colleges.
2. Data-oriented personality. Strong problem-solving skills with an emphasis on product development.
3. Great communication skills. Excellent written and verbal communication skills for coordinating across teams.
4. Good applied statistics skills such as distributions, statistical testing, and regression.
5. Good scripting and programming skills. Experience using statistical computer languages, Python, PySpark, R, SQL to manipulate data and draw insights from large data sets.
6. Excellent understanding of machine learning techniques and algorithms, such as - NN, Naive Bayes, SVM, Decision Forests, artificial neural networks, and their real-world
advantages or drawbacks. Knowledge of deep learning techniques is a plus.
7. Experience with common data science toolkits such as R, NumPy, Pandas, Scikitlearn, TensorFlow, Keras, etc.
8. Experience with data visualization tools such as D3.js, and GGplot.
9. Proficiency in using query languages such as SQL.
10. Experience with NoSQL databases such as MongoDB, Cassandra, and HBase is desired.
11. Experience with distributed data/computing tools like Map/Reduce, Hadoop, Hive, and Spark is a big plus.