Scripbox is a friendly investment service that simplifies the investment journey of anyone who wants to get started with investing for whatever savings need they might have.
As the first step, we make personal finance jargon-free and simple. We simplify complex investing concepts and automate them so everyone can grow their wealth. We do this by combining cutting edge technology, data-driven algorithms, awesome UX and friendly customer support.
Our task is ambitious and we like to work hard as well as smart. We want to build a team that relishes challenges and contributes to a new way of thinking.
We are looking for an expert with analyzing large data sets. You are excited about diving deep into data analysis and discovering root causes. You will partner with engineering and product executives to design long-term solutions and tools to democratise data across the company.
You would work in a team of data engineers and analysts working on these example challenges:
- Design and build reliable data pipelines.
- Analyse data for business insights and recommendations.
- Detect anomalies.
- 1+ years of hands-on experience in analysis of big datasets.
- Knowledge of relational databases and SQL.
- Experience with R, Java, Scala and Python. Most of our data pipelines and analysis use Python.
- Strong scripting ability in Ruby, Python, Bash etc.
- Passion for scaling data work and improving the efficiency of teams.
- Maintain high quality coding standards by use of automated testing and other engineering best practices.
Nice to have:
- Experience with open source data platforms such as Hadoop, Hive, Kafka, Spark, Presto, Tensorflow and Keras etc.
- Understanding of statistics, machine learning, deep learning.
- Entrepreneurial spirit. Everywhere you go, you can’t help but mobilize people, build things, solve problems, roll up your sleeves, go above and beyond, raise the bar. You are an insatiable doer and driver.
- Strong execution and organization. Your team will be working with engineers and product leads at the bleeding edge of the development cycle. To be successful in this role, you should be comfortable executing with little oversight and be able to adapt to problems quickly.