Data Scientist

 

 

Designation – Deputy Manager - TS

Job Description

1. Total of  8/9 years of development experience Data Engineering . B1/BII role
2. Minimum of 4/5 years in AWS Data Integrations and should be very good on Data modelling skills.
3. Should be very proficient in end to end AWS Data solution design, that not only includes strong data ingestion, integrations (both Data @ rest and Data in Motion) skills but also complete DevOps knowledge.
4. Should have experience in delivering at least 4 Data Warehouse or Data Lake Solutions on AWS.
5. Should be very strong experience on Glue, Lambda, Data Pipeline, Step functions, RDS, CloudFormation etc.
6. Strong Python skill .
7. Should be an expert in Cloud design principles, Performance tuning and cost modelling. AWS certifications will have an added advantage
8. Should be a team player with Excellent communication and should be able to manage his work independently with minimal or no supervision.
9. Life Science & Healthcare domain background will be a plus

Qualifications

BE/Btect/ME/MTech

 

Exp-Min 10 Years

Location Mumbai

Sal-Nego

 

 

Powerbi, Tableau, QlikView,

 

 

Solution Architect/Technology Lead – Data Analytics

 

Role

Looking for Business Intelligence lead (BI Lead) having hands on experience BI tools (Tableau, SAP Business Objects, Financial and Accounting modules, Power BI), SAP integration, and database knowledge including one or more of Azure Synapse/Datafactory, SQL Server, Oracle, cloud-based DB Snowflake. Good knowledge of AI-ML, Python is also expected.

• You will be expected to work closely with our business users. The development will be performed using an Agile methodology which is based on scrum (time boxing, daily scrum meetings, retrospectives, etc.) and XP (continuous integration, refactoring, unit testing, etc) best practices. Candidates must therefore be able to work collaboratively, demonstrate good ownership, leadership and be able to work well in teams.
• Responsibilities :
• Design, development and support of multiple/hybrid Data sources, data visualization Framework using Power BI, Tableau, SAP Business Objects etc. and using ETL tools, Scripting, Python Scripting etc.
• Implementing DevOps techniques and practices like Continuous Integration, Continuous Deployment, Test Automation, Build Automation and Test-Driven Development to enable the rapid delivery of working code-utilizing tools like Git. Primary Skills

Requirements

• 10+ years working as a hands-on developer in Information Technology across Database, ETL and BI (SAP Business Objects, integration with SAP Financial and Accounting modules, Tableau, Power BI) & prior team management experience
• Tableau/PowerBI integration with SAP and knowledge of SAP modules related to finance is a must
• 3+ years of hands-on development experience in Data Warehousing and Data Processing
• 3+ years of Database development experience with a solid understanding of core database concepts and relational database design, SQL, Performance tuning
• 3+ years of hands-on development experience with Tableau
• 3+ years of Power BI experience including parameterized reports and publishing it on PowerBI Service
• Excellent understanding and practical experience delivering under an Agile methodology
• Ability to work with business users to provide technical support
• Ability to get involved in all the stages of project lifecycle, including analysis, design, development, testing, Good To have Skills
• Experience with other Visualization tools and reporting tools like SAP Business Objects.

 

Company Profile
XressBees – a logistics company started in 2015 – is amongst the fastest growing companies of its sector. Our
vision to evolve into a strong full-service logistics organization reflects itself in the various lines of business like B2C
logistics 3PL, B2B Xpress, Hyperlocal and Cross border Logistics.
Our strong domain expertise and constant focus on innovation has helped us rapidly evolve as the most trusted
logistics partner of India. XB has progressively carved our way towards best-in-class technology platforms, an
extensive logistics network reach, and a seamless last mile management system.
While on this aggressive growth path, we seek to become the one-stop-shop for end-to-end logistics solutions. Our
big focus areas for the very near future include strengthening our presence as service providers of choice and
leveraging the power of technology to drive supply chain efficiencies.
Job Overview
XpressBees would enrich and scale its end-to-end logistics solutions at a high pace. This is a great opportunity to join
the team working on forming and delivering the operational strategy behind Artificial Intelligence / Machine Learning
and Data Engineering, leading projects and teams of AI Engineers collaborating with Data Scientists. In your role, you
will build high performance AI/ML solutions using groundbreaking AI/ML and BigData technologies. You will need to
understand business requirements and convert them to a solvable data science problem statement. You will be
involved in end to end AI/ML projects, starting from smaller scale POCs all the way to full scale ML pipelines in
production.
Seasoned AI/ML Engineers would own the implementation and productionzation of cutting-edge AI driven algorithmic
components for search, recommendation and insights to improve the efficiencies of the logistics supply chain and
serve the customer better.
You will apply innovative ML tools and concepts to deliver value to our teams and customers and make an impact to
the organization while solving challenging problems in the areas of AI, ML , Data Analytics and Computer Science.
Opportunities for application:
- Route Optimization
- Address / Geo-Coding Engine
- Anomaly detection, Computer Vision (e.g. loading / unloading)
- Fraud Detection (fake delivery attempts)
- Promise Recommendation Engine etc.
- Customer & Tech support solutions, e.g. chat bots.
- Breach detection / prediction
An Artificial Intelligence Engineer would apply himself/herself in the areas of -
- Deep Learning, NLP, Reinforcement Learning
- Machine Learning - Logistic Regression, Decision Trees, Random Forests, XGBoost, etc..
- Driving Optimization via LPs, MILPs, Stochastic Programs, and MDPs
- Operations Research, Supply Chain Optimization, and Data Analytics/Visualization
- Computer Vision and OCR technologies
The AI Engineering team enables internal teams to add AI capabilities to their Apps and Workflows easily via APIs
without needing to build AI expertise in each team – Decision Support, NLP, Computer Vision, for Public Clouds and
Enterprise in NLU, Vision and Conversational AI.Candidate is adept at working with large data sets to find
opportunities for product and process optimization and using models to test the effectiveness of different courses of
action. They must have knowledge using a variety of data mining/data analysis methods, using a variety of data tools,
building, and implementing models, using/creating algorithms, and creating/running simulations. They must be
comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion
for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

Roles & Responsibilities
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Building cloud services in Decision Support (Anomaly Detection, Time series forecasting, Fraud detection,
Risk prevention, Predictive analytics), computer vision, natural language processing (NLP) and speech that
work out of the box.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Build core of Artificial Intelligence and AI Services such as Decision Support, Vision, Speech, Text, NLP, NLU,
and others.
● Leverage Cloud technology –AWS, GCP, Azure
● Experiment with ML models in Python using machine learning libraries (Pytorch, Tensorflow), Big Data,
Hadoop, HBase, Spark, etc
● Work with stakeholders throughout the organization to identify opportunities for leveraging company data to
drive business solutions.
● Mine and analyze data from company databases to drive optimization and improvement of product
development, marketing techniques and business strategies.
● Assess the effectiveness and accuracy of new data sources and data gathering techniques.
● Develop custom data models and algorithms to apply to data sets.
● Use predictive modeling to increase and optimize customer experiences, supply chain metric and other
business outcomes.
● Develop company A/B testing framework and test model quality.
● Coordinate with different functional teams to implement models and monitor outcomes.
● Develop processes and tools to monitor and analyze model performance and data accuracy.
● Develop scalable infrastructure, including microservices and backend, that automates training and
deployment of ML models.
● Brainstorm and Design various POCs using ML/DL/NLP solutions for new or existing enterprise problems.
● Work with fellow data scientists/SW engineers to build out other parts of the infrastructure, effectively
communicating your needs and understanding theirs and address external and internal shareholder's
product challenges.
● Deliver machine learning and data science projects with data science techniques and associated libraries
such as AI/ ML or equivalent NLP (Natural Language Processing) packages. Such techniques include a good
to phenomenal understanding of statistical models, probabilistic algorithms, classification, clustering, deep
learning or related approaches as it applies to financial applications.
● The role will encourage you to learn a wide array of capabilities, toolsets and architectural patterns for
successful delivery.
What is required of you?
You will get an opportunity to build and operate a suite of massive scale, integrated data/ML platforms in a broadly
distributed, multi-tenant cloud environment.
● B.S., M.S., or Ph.D. in Computer Science, Computer Engineering
● Coding knowledge and experience with several languages: C, C++, Java,JavaScript, etc.
● Experience with building high-performance, resilient, scalable, and well-engineered systems
● Experience in CI/CD and development best practices, instrumentation, logging systems
● Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights
from large data sets.
● Experience working with and creating data architectures.
● Good understanding of various machine learning and natural language processing technologies, such as
classification, information retrieval, clustering, knowledge graph, semi-supervised learning and ranking.

● Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest,
Boosting, Trees, text mining, social network analysis, etc.
● Knowledge on using web services: Redshift, S3, Spark, Digital Ocean, etc.
● Knowledge on creating and using advanced machine learning algorithms and statistics: regression,
simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
● Knowledge on analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Core metrics,
AdWords, Crimson Hexagon, Facebook Insights, etc.
● Knowledge on distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, MySQL, Kafka etc.
● Knowledge on visualizing/presenting data for stakeholders using: Quicksight, Periscope, Business Objects,
D3, ggplot, Tableau etc.
● Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural
networks, etc.) and their real-world advantages/drawbacks.
● Knowledge of advanced statistical techniques and concepts (regression, properties of distributions,
statistical tests, and proper usage, etc.) and experience with applications.
● Experience building data pipelines that prep data for Machine learning and complete feedback loops.
● Knowledge of Machine Learning lifecycle and experience working with data scientists
● Experience with Relational databases and NoSQL databases
● Experience with workflow scheduling / orchestration such as Airflow or Oozie
● Working knowledge of current techniques and approaches in machine learning and statistical or
mathematical models
● Strong Data Engineering & ETL skills to build scalable data pipelines. Exposure to data streaming stack (e.g.
Kafka)
● Relevant experience in fine tuning and optimizing ML (especially Deep Learning) models to bring down
serving latency.
● Exposure to ML model productionzation stack (e.g. MLFlow, Docker)
● Excellent exploratory data analysis skills to slice & dice data at scale using SQL in Redshift/BigQuery.

Job Details:-

Designation - Data Scientist

Urgently required. (NP of maximum 15 days)

Location:- Mumbai

Experience:- 5-7 years.

Package Offered:- Rs.5,00,000/- to Rs.9,00,000/- pa.

 

Data Scientist

 

Job Description:-

Responsibilities:

• Identify valuable data sources and automate collection processes
• Undertake preprocessing of structured and unstructured data
• Analyze large amounts of information to discover trends and patterns
• Build predictive models and machine-learning algorithms
• Combine models through ensemble modeling
• Present information using data visualization techniques
• Propose solutions and strategies to business challenges
• Collaborate with engineering and product development teams

 

Requirements:

• Proven experience as a Data Scientist or Data Analyst
• Experience in data mining
• Understanding of machine-learning and operations research
• Knowledge of R, SQL and Python; familiarity with Scala, Java is an asset
• Experience using business intelligence tools (e.g. Tableau) and data frameworks (e.g. Hadoop)
• Analytical mind and business acumen
• Strong math skills (e.g. statistics, algebra)
• Problem-solving aptitude
• Excellent communication and presentation skills
• BSc/BA in Computer Science, Engineering or relevant field; graduate degree in Data Science or other quantitative field is preferred

Work on the cutting edge FinTech landscape on problems of prediction and analytics.This position will work on internal and external data to draw insights into the bottomline for improving customer experience, credit decisioning and predictive maintainance of the platform.

• Create and maintain optimal data pipeline architecture
• Assemble large, complex data sets that meet functional / non-functional business requirements.
• Author data services using a variety of programming languages
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using Snowflake Cloud Datawarehouse as well as SQL and Azure ‘big data’ technologies
• Work with stakeholders including the Executive, Product, Data and Design teams to assist with data-related technical issues and support their data infrastructure needs.
• Keep our data separated and secure across national boundaries through multiple data centers and Azure regions.
• Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
• Work with data and analytics experts to strive for greater functionality in our data systems.
• Work in an Agile environment with Scrum teams.
• Ensure data quality and help in achieving data governance.

• 3+ years of experience in a Data Engineer or Software Engineer role
• Undergraduate degree required (Graduate degree preferred) in Computer Science, Statistics, Informatics, Information Systems or another quantitative field.
• Experience using the following software/tools:
• Experience with “Snowflake Cloud Datawarehouse”
• Experience with Azure cloud services: ADLS, ADF, ADLA, AAS
• Experience with data pipeline and workflow management tools
• Advanced working SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases
• Understanding of Datawarehouse (DWH) systems, and migration from DWH to data lakes/Snowflake
• Understanding of ELT and ETL patterns and when to use each. Understanding of data models and transforming data into the models
• Strong analytic skills related to working with unstructured datasets
• Build processes supporting data transformation, data structures, metadata, dependency and workload management
• Experience supporting and working with cross-functional teams in a dynamic environment.

data analytics data science business analytics business intelligence

Requirements: Minimum 4-years work experience in building, managing and maintaining Analytics applications B.Tech/BE in CS/IT from Tier 1/2 Institutes Strong Fundamentals of Data Structures and Algorithms Good analytical & problem-solving skills Strong hands-on experience in Python In depth Knowledge of queueing systems (Kafka/ActiveMQ/RabbitMQ) Experience in building Data pipelines & Real time Analytics Systems Experience in SQL (MYSQL) & NoSQL (Mongo/Cassandra) databases is a plus Understanding of Service Oriented Architecture Delivered high-quality work with a significant contribution Expert in git, unit tests, technical documentation and other development best practices Experience in Handling small teams