Sr. Data scientist
Chargebee is a recurring billing and subscription management tool that helps SaaS and
SaaS-like businesses streamline Revenue Operations.
At Chargebee, we rely on insightful data to power our systems and solutions. Were seeking
experienced data scientists to deliver those insights to us on a daily basis. Our ideal team
member will have the mathematical and statistical expertise youd expect, along with natural
curiosity and a creative mind thats not so easy to find. As you mine, interpret and clean the
data, we will rely on you to ask questions, connect the dots, and uncover opportunities that lie
hidden with the ultimate goal of realizing the datas full potential. You are expected to bring in
a strong experience of using a variety of data mining methods and tools in building models
and running simulations. You must have a proven ability to drive business results with databased
insights and more importantly, you should be comfortable working with a wide range of
stakeholders and functional teams. You will be instrumental in helping the business continue
its evolution into an analytical and data-driven culture.
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 modelling 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 of structured and unstructured data.
6. Run data exploration to understand relationships and patterns within the data, develop
data visualisation 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
12. Lead debugging of data pipelines and model behaviour in the production
environment. Develop dashboards to enable easy tracking and communication of
Desired Skills & Experience
1. Were looking for someone with 5-7 years of experience manipulating data sets and
building statistical models, with a Bachelors / Masters / PhD degree in Statistics,
Mathematics, Computer Science or another quantitative field, from any of the top-tier
2. Data-oriented personality. Strong problem-solving skills with an emphasis on product
3. Great communication skills. Excellent written and verbal communication skills for
coordinating across teams.
4. Good applied statistics skills such as distributions, statistical testing, regression.
5. Good scripting and programming skills. Experience using statistical computer
languages, Python, PySpark, R, SQL to manipulate data and draw insights from large
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 visualisation tools such as D3.js, GGplot.
9. Proficiency in using query languages such as SQL.
10. Experience with NoSQL databases such as MongoDB, Cassandra, HBase is desired.
11. Experience with distributed data / computing tools like Map / Reduce, Hadoop, Hive,
Spark is a big plus.