Data Scientist

ML / AI  engineer with hands-on experience of working with Time Series Data, layering of data and adopting complex parameters. At least 3-5 years of experience of working with customer data and handling ETL operations. Experience of converting machine learning models into APIs

Responsibilities

1. Create data funnels to feed into models via web, structured and unstructured data
2. Maintain coding standards using  SDLC, Git, AWS deployments etc
3. Keep abreast of developments in the field
4. Deploy models in production and monitor them
5. Documentations of processes and logic
6. Take ownership of the solution from code to deployment and performance

 

Azure – Data Engineer

• At least 2 years hands on experience working with an Agile data engineering team working on big data pipelines using Azure in a commercial environment.
• Dealing with senior stakeholders/leadership
• Understanding of Azure data security and encryption best practices. [ADFS/ACLs]

Data Bricks –experience writing in and using data bricks Using Python to transform, manipulate data.

Data Factory – experience using data factory in an enterprise solution to build data pipelines. Experience calling rest APIs.

Synapse/data warehouse – experience using synapse/data warehouse to present data securely and to build & manage data models.

Microsoft SQL server – We’d expect the candidate to have come from a SQL/Data background and progressed into Azure

PowerBI – Experience with this is preferred

Additionally

• Experience using GIT as a source control system
• Understanding of DevOps concepts and application
• Understanding of Azure Cloud costs/management and running platforms efficiently

Job Responsibilities

• Design, build & test ETL processes using Python & SQL for the corporate data warehouse
• Inform, influence, support, and execute our product decisions
• Maintain advertising data integrity by working closely with R&D to organize and store data in a format that provides accurate data and allows the business to quickly identify issues.
• Evaluate and prototype new technologies in the area of data processing
• Think quickly, communicate clearly and work collaboratively with product, data, engineering, QA and operations teams
• High energy level, strong team player and good work ethic
• Data analysis, understanding of business requirements and translation into logical pipelines & processes
• Identification, analysis & resolution of production & development bugs
• Support the release process including completing & reviewing documentation
• Configure data mappings & transformations to orchestrate data integration & validation
• Provide subject matter expertise
• Document solutions, tools & processes
• Create & support test plans with hands-on testing
• Peer reviews of work developed by other data engineers within the team
• Establish good working relationships & communication channels with relevant departments

 

Skills and Qualifications we look for

• University degree 2.1 or higher (or equivalent) in a relevant subject. Master’s degree in any data subject will be a strong advantage.
• 4 - 6 years experience with data engineering.
• Strong coding ability and software development experience in Python.
• Strong hands-on experience with SQL and Data Processing.
• Google cloud platform (Cloud composer, Dataflow, Cloud function, Bigquery, Cloud storage, dataproc)
• Good working experience in any one of the ETL tools (Airflow would be preferable).
• Should possess strong analytical and problem solving skills.
• Good to have skills - Apache pyspark, CircleCI, Terraform
• Motivated, self-directed, able to work with ambiguity and interested in emerging technologies, agile and collaborative processes.
• Understanding & experience of agile / scrum delivery methodology

 

• Gather information from multiple data sources make Approval Decisions mechanically
• Read and interpret credit related information to the borrowers
• Interpret, analyze and assess all forms of complex information
• Embark on risk assessment analysis
• Maintain the credit exposure of the company within certain risk level with set limit in mind
• Build strategies to minimize risk and increase approval rates
• Design Champion and Challenger tests, implement and read test results
• Build Line assignment strategies

Skills required:
- Credit Risk Modeling
- Statistical Data Understanding and interpretation
- Basic Regression and Advanced Machine Learning Models
- Conversant with coding on Python using libraries like Sklearn etc.
- Build and understand decision trees

1. Proven experience with deploying and tuning Open Source components into enterprise ready production tooling Experience with datacentre (Metal as a Service – MAAS) and cloud deployment technologies (AWS or GCP Architect certificates required)
2. Deep understanding of Linux from kernel mechanisms through user space management
3. Experience on CI/CD (Continuous Integrations and Deployment) system solutions (Jenkins).
4. Using Monitoring tools (local and on public cloud platforms) Nagios, Prometheus, Sensu, ELK, Cloud Watch, Splunk, New Relic etc. to trigger instant alerts, reports and dashboards.  Work closely with the development and infrastructure teams to analyze and design solutions with four nines (99.99%) up-time, globally distributed, clustered, production and non-production virtualized infrastructure. 
5. Wide understanding of IP networking as well as data centre infrastructure

1. Expert with software development tools and sourcecode management, understanding, managing issues, code changes and grouping them into deployment releases in a stable and measurable way to maximize production Must be expert at developing and using ansible roles and configuring deployment templates with jinja2.
2. Solid understanding of data collection tools like Flume, Filebeat, Metricbeat, JMX Exporter agents.
3. Extensive experience operating and tuning the kafka streaming data platform, specifically as a message queue for big data processing
4. Strong understanding and must have experience:
5. Apache spark framework, specifically spark core and spark streaming, 
6. Orchestration platforms, mesos and kubernetes, 
7. Data storage platforms, elasticstack, carbon, clickhouse, cassandra, ceph, hdfs
8. Core presentation technologies kibana, and grafana.
9. Excellent scripting and programming skills (bash, python, java, go, rust). Must have previous experience with “rust” in order to support, improve in house developed products

Red Hat Certified Architect certificate or equivalent required CCNA certificate required 3-5 years of experience running open source big data platforms

REQUIREMENT:

•  Previous experience of working in large scale data engineering
•  4+ years of experience working in data engineering and/or backend technologies with cloud experience (any) is mandatory.
•  Previous experience of architecting and designing backend for large scale data processing.
•  Familiarity and experience of working in different technologies related to data engineering – different database technologies, Hadoop, spark, storm, hive etc.
•  Hands-on and have the ability to contribute a key portion of data engineering backend.
•  Self-inspired and motivated to drive for exceptional results.
•  Familiarity and experience working with different stages of data engineering – data acquisition, data refining, large scale data processing, efficient data storage for business analysis.
•  Familiarity and experience working with different DB technologies and how to scale them.

RESPONSIBILITY:

•  End to end responsibility to come up with data engineering architecture, design, development and then implementation of it.
•  Build data engineering workflow for large scale data processing.
•  Discover opportunities in data acquisition.
•  Bring industry best practices for data engineering workflow.
•  Develop data set processes for data modelling, mining and production.
•  Take additional tech responsibilities for driving an initiative to completion
•  Recommend ways to improve data reliability, efficiency and quality
•  Goes out of their way to reduce complexity.
•  Humble and outgoing - engineering cheerleaders.

We are an early stage startup working in the space of analytics, big data, machine learning, data visualization on multiple platforms and SaaS. We have our offices in Palo Alto and WTC, Kharadi, Pune and got some marque names as our customers. We are looking for really good Python programmer who MUST have scientific programming experience (Python, etc.) Hands-on with numpy and the Python scientific stack is a must. Demonstrated ability to track and work with 100s-1000s of files and GB-TB of data. Exposure to ML and Data mining algorithms. Need to be comfortable working in a Unix environment and SQL. You will be required to do following: Using command line tools to perform data conversion and analysis Supporting other team members in retrieving and archiving experimental results Quickly writing scripts to automate routine analysis tasks Creating insightful, simple graphics to represent complex trends Explore/design/invent new tools and design patterns to solve complex big data problems Experience working on a long-term, lab-based project (academic experience acceptable)

Job Description: Data Science Analyst/ Data Science Senior Analyst Job description KSTYCH is seeking a Data Science Analyst to join our Data Science team. Individuals in this role are expected to be comfortable working as a software engineer and a quantitative researcher, should have a significant theoretical foundation in mathematical statistics. The ideal candidate will have a keen interest in the study of Pharma sector, network biology, text mining, machine learning, and a passion for identifying and answering questions that help us build the best consulting resource and continuous support to other teams. Responsibilities Work closely with a product scientific, medical, business development and commercial to identify and answer important healthcare/pharma/biology questions. Answer questions by using appropriate statistical techniques and tools on available data. Communicate findings to project managers and team managers. Drive the collection of new data and the refinement of existing data sources Analyze and interpret the results of an experiments Develop best practices for instrumentation and experimentation and communicate those to other teams Requirements B. Tech, M.Tech, M.S. or Ph.D. in a relevant technical field, or 1+ years experience in a relevant role Extensive experience solving analytical problems using quantitative approaches Comfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources A strong passion for empirical research and for answering hard questions with data A flexible analytic approach that allows for results at varying levels of precision Ability to communicate complex quantitative analysis in a clear, precise, and actionable manner Fluency with at least one scripting language such as Python or PHP Familiarity with relational databases and SQL Experience working with large data sets, experience working with distributed computing tools a plus (KNIME, Map/Reduce, Hadoop, Hive, etc)