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About UPS:
Moving our world forward by delivering what matters! UPS is a company with a proud past and an even brighter future. Our values define us. Our culture differentiates us. Our strategy drives us. At UPS we are customer first, people led and innovation driven. UPS’s India based Technology Development Centers will bring UPS one step closer to creating a global technology workforce that will help accelerate our digital journey and help us engineer technology solutions that drastically improve our competitive advantage in the field of Logistics.
‘Future You’ grows as a visible and valued Technology professional with UPS, driving us towards an exciting tomorrow. As a global Technology organization we can put serious resources behind your development. If you are solutions orientated, UPS Technology is the place for you. ‘Future You’ delivers ground-breaking solutions to some of the biggest logistics challenges around the globe. You’ll take technology to unimaginable places and really make a difference for UPS and our customers.
Job Summary:
This position participates in the support of batch and real-time data pipelines utilizing various data analytics processing frameworks in support of data science practices for Marketing and Finance business units. This position supports the integration of data from various data sources, as well as performs extract, transform, load (ETL) data conversions, and facilitates data cleansing and enrichment. This position performs full systems life cycle management activities, such as analysis, technical requirements, design, coding, testing, implementation of systems and applications software. This position participates and contributes to synthesizing disparate data sources to support reusable and reproducible data assets.
RESPONSIBILITIES
• Supervises and supports data engineering projects and builds solutions by leveraging a strong foundational knowledge in software/application development. He/she is hands on.
• Develops and delivers data engineering documentation.
• Gathers requirements, defines the scope, and performs the integration of data for data engineering projects.
• Recommends analytic reporting products/tools and supports the adoption of emerging technology.
• Performs data engineering maintenance and support.
• Provides the implementation strategy and executes backup, recovery, and technology solutions to perform analysis.
• Performs ETL tool capabilities with the ability to pull data from various sources and perform a load of the transformed data into a database or business intelligence platform.
• Codes using programming language used for statistical analysis and modeling such as Python/Spark
REQUIRED QUALIFICATIONS:
• Literate in the programming languages used for statistical modeling and analysis, data warehousing and Cloud solutions, and building data pipelines.
• Proficient in developing notebooks in Data bricks using Python and Spark and Spark SQL.
• Strong understanding of a cloud services platform (e.g., GCP, or AZURE, or AWS) and all the data life cycle stages. Azure is preferred
• Proficient in using Azure Data Factory and other Azure features such as LogicApps.
• Preferred to have knowledge of Delta lake, Lakehouse and Unity Catalog concepts.
• Strong understanding of cloud-based data lake systems and data warehousing solutions.
• Has used AGILE concepts for development, including KANBAN and Scrums
• Strong understanding of the data interconnections between organizations’ operational and business functions.
• Strong understanding of the data life cycle stages - data collection, transformation, analysis, storing the data securely, providing data accessibility
• Strong understanding of the data environment to ensure that it can scale for the following demands: Throughput of data, increasing data pipeline throughput, analyzing large amounts of data, Real-time predictions, insights and customer feedback, data security, data regulations, and compliance.
• Strong knowledge of algorithms and data structures, as well as data filtering and data optimization.
• Strong understanding of analytic reporting technologies and environments (e.g., Power BI, Looker, Qlik, etc.)
• Understanding of distributed systems and the underlying business problem being addressed, as well as guides team members on how their work will assist by performing data analysis and presenting findings to the stakeholders. •
REQUIRED SKILLS:
3 years of experience with Databricks, Apache Spark, Python, and SQL
Preferred SKILLS:
DeltaLake Unity Catalog, R, Scala, Azure Logic Apps, Cloud Services Platform (e.g., GCP, or AZURE, or AWS), and AGILE concepts.
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About Delivery Solutions
Out-of-the-box solutions are provided by Delivery Solutions to retailers, allowing them to provide customer experiences such as curbside delivery, same-day delivery, shipping, in-store pickup, and post-purchase pickup. The company collaborates with some of the most recognizable names in the retail industry, such as Michael's, Sephora, Loblaw, GameStop, Office Depot, Sally Beauty, Total Wine, Belk, and Abercrombie & Fitch.
Its SAAS-based solution is incredibly adjustable and works in combination with e-commerce sites, warehouse management systems, order management systems, and point-of-sale systems to give a highly scalable experience and a base of delighted customers. They have direct connections to the most prominent businesses in same-day delivery, like DoorDash, Uber, Postmates, and Shipt, amongst others, in addition to the most prominent shipping firms, including UPS, FedEx, USPS, and others.
Perks & Benefits @Delivery Solutions:
- Permanent Remote work - (Work from anywhere)
- Broadband reimbursement
- Flexi work hours - (Login/Logout flexibility)
- 21 Paid leaves in a year (Jan to Dec) and 7 COVID leaves
- Two appraisal cycles in a year
- Encashment of unused leaves on Gross
- RNR - Amazon Gift Voucher
- Employee Referral Bonus
- Technical & Soft skills training
- Sodexo meal card
- Surprise on birthday/ service anniversary/new baby/wedding gifts
- Annual trip
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Required skills and experience: · Solid experience working in Big Data ETL environments with Spark and Java/Scala/Python · Strong experience with AWS cloud technologies (EC2, EMR, S3, Kinesis, etc) · Experience building monitoring/alerting frameworks with tools like Newrelic and escalations with slack/email/dashboard integrations, etc · Executive-level communication, prioritization, and team leadership skills
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As a part of WWS, your expertise in machine learning helps us extract value from our data. You will lead all the processes from data collection, cleaning, and preprocessing, to training models and situating them to production. The ideal candidate will be passionate about artificial intelligence and stay up-to-date with the latest developments in the field.
What We Expect
- A Bachelor's/Master's degree in IT, computer science, or an advanced related field is preferred.
- At least 3+ years of experience working with ML libraries and packages.
- Familiarity with coding and programming languages, including Python, Java, C++, and SAS.
- Strong experience in programming and statistics.
- Well-versed in Data Science and neural schematics in networking and software.
- Flexibility in shifts is appreciated.
A Full Stack Developer’s Ideal Day At WWS
Design and Develop. The primary protocols include implementing machine learning algorithms and running AI systems experiments and tests. The Designing and development of machine learning systems along with performing statistical analyses falls under day to day activities of the developer.
Algorithm Assertion. The engineers act as critical members of the data science team as their tasks involve researching, asserting, and designing the artificial intelligence responsible for machine learning and maintaining and improving existing artificial intelligence systems.
Research and Development. To analyze large, complex datasets and extract insights and decide on the appropriate technique. research and implement best practices to improve the existing machine learning infrastructure. At most providing support to engineers and product managers in implementing machine learning as the product.
What You Can Expect
- Full-time, salaried positions creamed with welfare programs.
- Competitive salary and tailored training in the core space with recognition potential and annual bonus.
- Periodic performance appraisals.
- Attendance Incentives.
- Working with the best and budding talent in the industry.
- A conducive intangible environment with dynamic benefits.
Why Consider Machine Learning Engineer as a career with WWS?
With a very appealing work environment at WWS, our setting made it easier to build relationships with other staff members and clients. You may also have an opportunity to learn other aspects of environmental office work on the job, which can enhance your experience and qualifications.
Many businesses must proactively react to changing factors — like patterns of customer behavior or prices. Tracking model performance and retraining it once fresher data is available is key to success. This falls under the MLE range of responsibilities for which the requirement has been crucial for many organizations.
Please attach your resume and let us know through email your current address, phone number, and the best time to contact you by phone.
Apply To this Job
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Responsibilities
Researches, develops and maintains machine learning and statistical models for
business requirements
Work across the spectrum of statistical modelling including supervised,
unsupervised, & deep learning techniques to apply the right level of solution to
the right problem Coordinate with different functional teams to monitor outcomes and refine/
improve the machine learning models Implements models to uncover patterns and predictions creating business value and innovation
Identify unexplored data opportunities for the business to unlock and maximize
the potential of digital data within the organization
Develop NLP concepts and algorithms to classify and summarize structured/unstructured text data
Qualifications
3+ years of experience solving complex business problems using machine
learning.
Fluency in programming languages such as Python, NLP and Bert, is a must
Strong analytical and critical thinking skills
Experience in building production quality models using state-of-the-art technologies
Familiarity with databases .
desirable Ability to collaborate on projects and work independently when required.
Previous experience in Fintech/payments domain is a bonus
You should have Bachelor’s or Master’s degree in Computer Science, Statistics
or Mathematics or another quantitative field from a top tier Institute
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Job Description
Mandatory Requirements
-
Experience in AWS Glue
-
Experience in Apache Parquet
-
Proficient in AWS S3 and data lake
-
Knowledge of Snowflake
-
Understanding of file-based ingestion best practices.
-
Scripting language - Python & pyspark
CORE RESPONSIBILITIES
-
Create and manage cloud resources in AWS
-
Data ingestion from different data sources which exposes data using different technologies, such as: RDBMS, flat files, Streams, and Time series data based on various proprietary systems. Implement data ingestion and processing with the help of Big Data technologies
-
Data processing/transformation using various technologies such as Spark and Cloud Services. You will need to understand your part of business logic and implement it using the language supported by the base data platform
-
Develop automated data quality check to make sure right data enters the platform and verifying the results of the calculations
-
Develop an infrastructure to collect, transform, combine and publish/distribute customer data.
-
Define process improvement opportunities to optimize data collection, insights and displays.
-
Ensure data and results are accessible, scalable, efficient, accurate, complete and flexible
-
Identify and interpret trends and patterns from complex data sets
-
Construct a framework utilizing data visualization tools and techniques to present consolidated analytical and actionable results to relevant stakeholders.
-
Key participant in regular Scrum ceremonies with the agile teams
-
Proficient at developing queries, writing reports and presenting findings
-
Mentor junior members and bring best industry practices.
QUALIFICATIONS
-
5-7+ years’ experience as data engineer in consumer finance or equivalent industry (consumer loans, collections, servicing, optional product, and insurance sales)
-
Strong background in math, statistics, computer science, data science or related discipline
-
Advanced knowledge one of language: Java, Scala, Python, C#
-
Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake
-
Proficient with
-
Data mining/programming tools (e.g. SAS, SQL, R, Python)
-
Database technologies (e.g. PostgreSQL, Redshift, Snowflake. and Greenplum)
-
Data visualization (e.g. Tableau, Looker, MicroStrategy)
-
Comfortable learning about and deploying new technologies and tools.
-
Organizational skills and the ability to handle multiple projects and priorities simultaneously and meet established deadlines.
-
Good written and oral communication skills and ability to present results to non-technical audiences
-
Knowledge of business intelligence and analytical tools, technologies and techniques.
Familiarity and experience in the following is a plus:
-
AWS certification
-
Spark Streaming
-
Kafka Streaming / Kafka Connect
-
ELK Stack
-
Cassandra / MongoDB
-
CI/CD: Jenkins, GitLab, Jira, Confluence other related tools
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- Data Engineer
Required skill set: AWS GLUE, AWS LAMBDA, AWS SNS/SQS, AWS ATHENA, SPARK, SNOWFLAKE, PYTHON
Mandatory Requirements
- Experience in AWS Glue
- Experience in Apache Parquet
- Proficient in AWS S3 and data lake
- Knowledge of Snowflake
- Understanding of file-based ingestion best practices.
- Scripting language - Python & pyspark
CORE RESPONSIBILITIES
- Create and manage cloud resources in AWS
- Data ingestion from different data sources which exposes data using different technologies, such as: RDBMS, REST HTTP API, flat files, Streams, and Time series data based on various proprietary systems. Implement data ingestion and processing with the help of Big Data technologies
- Data processing/transformation using various technologies such as Spark and Cloud Services. You will need to understand your part of business logic and implement it using the language supported by the base data platform
- Develop automated data quality check to make sure right data enters the platform and verifying the results of the calculations
- Develop an infrastructure to collect, transform, combine and publish/distribute customer data.
- Define process improvement opportunities to optimize data collection, insights and displays.
- Ensure data and results are accessible, scalable, efficient, accurate, complete and flexible
- Identify and interpret trends and patterns from complex data sets
- Construct a framework utilizing data visualization tools and techniques to present consolidated analytical and actionable results to relevant stakeholders.
- Key participant in regular Scrum ceremonies with the agile teams
- Proficient at developing queries, writing reports and presenting findings
- Mentor junior members and bring best industry practices
QUALIFICATIONS
- 5-7+ years’ experience as data engineer in consumer finance or equivalent industry (consumer loans, collections, servicing, optional product, and insurance sales)
- Strong background in math, statistics, computer science, data science or related discipline
- Advanced knowledge one of language: Java, Scala, Python, C#
- Production experience with: HDFS, YARN, Hive, Spark, Kafka, Oozie / Airflow, Amazon Web Services (AWS), Docker / Kubernetes, Snowflake
- Proficient with
- Data mining/programming tools (e.g. SAS, SQL, R, Python)
- Database technologies (e.g. PostgreSQL, Redshift, Snowflake. and Greenplum)
- Data visualization (e.g. Tableau, Looker, MicroStrategy)
- Comfortable learning about and deploying new technologies and tools.
- Organizational skills and the ability to handle multiple projects and priorities simultaneously and meet established deadlines.
- Good written and oral communication skills and ability to present results to non-technical audiences
- Knowledge of business intelligence and analytical tools, technologies and techniques.
Familiarity and experience in the following is a plus:
- AWS certification
- Spark Streaming
- Kafka Streaming / Kafka Connect
- ELK Stack
- Cassandra / MongoDB
- CI/CD: Jenkins, GitLab, Jira, Confluence other related tools
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Roles and Responsibilities:
- Design, develop, and maintain the end-to-end MLOps infrastructure from the ground up, leveraging open-source systems across the entire MLOps landscape.
- Creating pipelines for data ingestion, data transformation, building, testing, and deploying machine learning models, as well as monitoring and maintaining the performance of these models in production.
- Managing the MLOps stack, including version control systems, continuous integration and deployment tools, containerization, orchestration, and monitoring systems.
- Ensure that the MLOps stack is scalable, reliable, and secure.
Skills Required:
- 3-6 years of MLOps experience
- Preferably worked in the startup ecosystem
Primary Skills:
- Experience with E2E MLOps systems like ClearML, Kubeflow, MLFlow etc.
- Technical expertise in MLOps: Should have a deep understanding of the MLOps landscape and be able to leverage open-source systems to build scalable, reliable, and secure MLOps infrastructure.
- Programming skills: Proficient in at least one programming language, such as Python, and have experience with data science libraries, such as TensorFlow, PyTorch, or Scikit-learn.
- DevOps experience: Should have experience with DevOps tools and practices, such as Git, Docker, Kubernetes, and Jenkins.
Secondary Skills:
- Version Control Systems (VCS) tools like Git and Subversion
- Containerization technologies like Docker and Kubernetes
- Cloud Platforms like AWS, Azure, and Google Cloud Platform
- Data Preparation and Management tools like Apache Spark, Apache Hadoop, and SQL databases like PostgreSQL and MySQL
- Machine Learning Frameworks like TensorFlow, PyTorch, and Scikit-learn
- Monitoring and Logging tools like Prometheus, Grafana, and Elasticsearch
- Continuous Integration and Continuous Deployment (CI/CD) tools like Jenkins, GitLab CI, and CircleCI
- Explain ability and Interpretability tools like LIME and SHAP
- KSQL
- Data Engineering spectrum (Java/Spark)
- Spark Scala / Kafka Streaming
- Confluent Kafka components
- Basic understanding of Hadoop
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Ideal candidates should have technical experience in migrations and the ability to help customers get value from Datametica's tools and accelerators.
Job Description
Experience : 7+ years
Location : Pune / Hyderabad
Skills :
- Drive and participate in requirements gathering workshops, estimation discussions, design meetings and status review meetings
- Participate and contribute in Solution Design and Solution Architecture for implementing Big Data Projects on-premise and on cloud
- Technical Hands on experience in design, coding, development and managing Large Hadoop implementation
- Proficient in SQL, Hive, PIG, Spark SQL, Shell Scripting, Kafka, Flume, Scoop with large Big Data and Data Warehousing projects with either Java, Python or Scala based Hadoop programming background
- Proficient with various development methodologies like waterfall, agile/scrum and iterative
- Good Interpersonal skills and excellent communication skills for US and UK based clients
About Us!
A global Leader in the Data Warehouse Migration and Modernization to the Cloud, we empower businesses by migrating their Data/Workload/ETL/Analytics to the Cloud by leveraging Automation.
We have expertise in transforming legacy Teradata, Oracle, Hadoop, Netezza, Vertica, Greenplum along with ETLs like Informatica, Datastage, AbInitio & others, to cloud-based data warehousing with other capabilities in data engineering, advanced analytics solutions, data management, data lake and cloud optimization.
Datametica is a key partner of the major cloud service providers - Google, Microsoft, Amazon, Snowflake.
We have our own products!
Eagle – Data warehouse Assessment & Migration Planning Product
Raven – Automated Workload Conversion Product
Pelican - Automated Data Validation Product, which helps automate and accelerate data migration to the cloud.
Why join us!
Datametica is a place to innovate, bring new ideas to live and learn new things. We believe in building a culture of innovation, growth and belonging. Our people and their dedication over these years are the key factors in achieving our success.
Benefits we Provide!
Working with Highly Technical and Passionate, mission-driven people
Subsidized Meals & Snacks
Flexible Schedule
Approachable leadership
Access to various learning tools and programs
Pet Friendly
Certification Reimbursement Policy
Check out more about us on our website below!
www.datametica.com
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About Us |
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upGrad is an online education platform building the careers of tomorrow by offering the most industry-relevant programs in an immersive learning experience. Our mission is to create a new digital-first learning experience to deliver tangible career impact to individuals at scale. upGrad currently offers programs in Data Science, Machine Learning, Product Management, Digital Marketing, and Entrepreneurship, etc. upGrad is looking for people passionate about management and education to help design learning programs for working professionals to stay sharp and stay relevant and help build the careers of tomorrow.
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• Drive the data engineering implementation
• Strong experience in building data pipelines
• AWS stack experience is must
• Deliver Conceptual, Logical and Physical data models for the implementation
teams.
• SQL stronghold is must. Advanced SQL working knowledge and experience
working with a variety of relational databases, SQL query authoring
• AWS Cloud data pipeline experience is must. Data pipelines and data centric
applications using distributed storage platforms like S3 and distributed processing
platforms like Spark, Airflow, Kafka
• Working knowledge of AWS technologies such as S3, EC2, EMR, RDS, Lambda,
Elasticsearch
• Ability to use a major programming (e.g. Python /Java) to process data for
modelling.
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