Pune, Bengaluru (Bangalore)
6 - 8 yrs
₹15L - ₹25L / yr
Full time
Skills
Machine Learning (ML)
Data Science
Natural Language Processing (NLP)
Computer Vision
Artificial Intelligence (AI)
Random boosting
Decision trees
XGBoost
Clustering
Big Data
recommendation algorithm
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.
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.
Read more
About xpressbees
Founded
2015
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Busigence is a Decision Intelligence Company. We create decision intelligence products for real people by combining data, technology, business, and behavior enabling strengthened decisions.
Scaling established startup by IIT alumni innovating & disrupting marketing domain through artificial intelligence. We bring those people onboard who are dedicated to deliver wisdom to humanity by solving the world’s most pressing problems differently thereby significantly impacting thousands of souls, everyday.
We are a deep rooted organization with six years of success story having worked with folks from top tier background (IIT, NSIT, DCE, BITS, IIITs, NITs, IIMs, ISI etc.) maintaining an awesome culture with a common vision to build great data products. In past we have served fifty five customers and presently developing our second product, Robonate. First was emmoQ - an emotion intelligence platform. Third offering, H2HData, an innovation lab where we solve hard problems through data, science, & design.
We work extensively & intensely on big data, data science, machine learning, deep learning, reinforcement learning, data analytics, natural language processing, cognitive computing, and business intelligence.
First-and-Foremost
Before you dive-in exploring this opportunity and press Apply, we wish you to evaluate yourself - We are looking for right candidate, not the best candidate. We love to work with someone who can mandatorily gel with our vision, beliefs, thoughts, methods, and values --- which are aligned with what can be expected in a true startup with ambitious goals.
Skills are always secondary to us. Primarily, you must be someone who is not essentially looking for a job or career, rather starving for a challenge, you yourself probably don't know since when.
A book can be written on what an applicant must have before joining a . For brevity, in nutshell, we need these three in you:
1. You must be [super sharp] (Just an analogue, but Irodov, Mensa, Feynman, Polya, ACM, NIPS, ICAAC, BattleCode, DOTA etc should have been your Done stuff. Can you relate solution 1 to problem 2? or Do you get confused even when solved similar problem in past? Are you able to grasp problem statement in one go? or get hanged?)
2. You must be [extremely energetic] (Do you raise eyebrows when asked to stretch your limits, both in terms of complexity or extra hours to put in? What comes first in your mind, let's finish it today or this can be done tomorrow too? Its Friday 10 PM at work -Tired?)
3. You must be [honourably honest] (Do you tell others what you think, or what they want to hear? Later is good for sales team for their customers, not for this role. Are you honest with your work? intrinsically with yourself first?)
You know yourself the best. If not ask your loved ones and then decide. We clearly need exceedingly motivated people with entrepreneurial traits, not employee mindset - not at all.
This is an immediate requirement. We shall have an accelerated interview process for fast closure - you would be required to be proactive and responsive.
Real ROLE
We are looking for researchers with real passion for deep learning. You would be required to work with our research team on several deep learning applications.
Mandatory
R1. Must know in-and-out of functional programming in Python with strong flair for data structures, linear algebra, & algorithms implementation
R2. Must be able to apply state-of-art deep learning models to our existing research and develop entirely new methods, which are advanced & modern
R3. Must have designed multiple deep learning techniques solving complex business scenarios preferably from marketing domain across industries
R4. Must have designed proven pre-processing techniques on unstructured (image, audio, video, text, logs) and time-series data
R5. Must have designed CNN, RNN (atleast three variants including LSTM), Auto Encoders, DBN, MLP, RBM
R6. Must have tweaked & trained on deep learning architectures mainly AlexNet, ResNet, RCNN, GAN
R7. Must have worked on classification, clustering, computer vision, speech recognition, text summarization, time-series analysis
R8. Must have firm understanding of the underlying mathematics will be needed to adapt modelling techniques to fit the problem space with large data (10M+ entities)
R9. Must be extremely strong working knowledge on Python scientific stack (NumPy, SciPy, Matplotlib, Scikit-learn, Pandas, Stats model, Seaborn, PyBrain) and atleast basic working knowledge on TensorFlow. R/ SAS/ SPSS etc are not acceptable.
Preferred
R10. Good to have knowledge and practical experience in application of Supervised Learning algorithms, Bayesian methods, Gaussian processes, Combinatorial optimization, Meta Learning, Transfer Learning, Convex optimization, Sparse methods, Kernel methods, Stochastic process, Statistical learning theory, and Approximate inference
R11. Good to have working knowledge of first-class, high order, & pure functions, recurisons, lazy evaluations, and immutable data structures
R12. Good to have worked on Tensorboard, Saving & Restoring models, Continue Training
R13. Good to have worked on Keras on top of TensorFlow
R14. Good to have worked on Reinforcement Learning and Generative Adverbial Networks
R15. Great problem solver with extremely high curiosity on data processing & patterns
Ideal YOU
Y1. Degree in engineering, or any other data-heavy field at Bachelors/Masters/Ph.D level or above from a top tier institute
Y2. Relevant experience of 0.5 - 5 years working on real-world problems in a research lab or a reputed company or a proven startup
Y3. You are a fanatical implementer who love to spend time with findings & innovations, more than your loved ones
Y4. You are true believer that human intelligence can be augmented through computer science & mathematics and your survival vinaigrette depends on getting the most from the data
Y5. You are an entrepreneur mindset with ownership, intellectuality, & creativity as way to work. These are not fancy words, we mean it
Actual WE
W1. Real startup with Meaningful products
W2. Revolutionary not just disruptive
W3. Rules creators not followers
W4. Small teams with real brains not herd of blockheads
W5. Completely trust us and should be trusted back
Why Us
In addition to the regular stuff which every good startup offers – Lots of learning, Food, Parties, Open culture, Flexible working hours, and what not….
We offer you:
You shall be working on our revolutionary products which are pioneer in their respective categories. This is a fact.
We try real hard to hire fun loving crazy folks who are driven by more than a paycheck. You shall be working with creamiest talent on extremely challenging problems at most happening workplace.
How to Apply
You should apply online by clicking "Apply Now".
For queries regarding an open position, please write to [email protected]
For more information, visit http://www.busigence.com
Careers: http://careers.busigence.com
Research: http://research.busigence.com
Jobs: http://careers.busigence.com/jobs/research
Feel right fit for the position, mandatorily attach PDF resume highlighting your
A. Key Skills
B. Knowledge Inputs
C. Major Accomplishments
D. Problems Solved
E. Submissions – Github/ StackOverflow/ Kaggle/ Euler Project etc. (if applicable)
If you don't see this open position that interests you, join our Talent Pool and let us know how you can make a difference here. Referrals are more than welcome. Keep us in loop.
Read more
Pune
3 - 8 yrs
₹5L - ₹30L / yr
Data Science
Machine Learning (ML)
Python
Big Data
SQL
+3 more
Job Description for Data Scientist/ NLP Engineer
Responsibilities for Data Scientist/ NLP Engineer
Work with customers to identify opportunities for leveraging their data to drive business
solutions.
• Develop custom data models and algorithms to apply to data sets.
• Basic data cleaning and annotation for any incoming raw data.
• Use predictive modeling to increase and optimize customer experiences, revenue
generation, ad targeting and other business outcomes.
• Develop company A/B testing framework and test model quality.
• Deployment of ML model in production.
Qualifications for Junior Data Scientist/ NLP Engineer
• BS, MS in Computer Science, Engineering, or related discipline.
• 3+ Years of experience in Data Science/Machine Learning.
• Experience with programming language Python.
• Familiar with at least one database query language, such as SQL
• Knowledge of Text Classification & Clustering, Question Answering & Query Understanding,
Search Indexing & Fuzzy Matching.
• Excellent written and verbal communication skills for coordinating acrossteams.
• Willing to learn and master new technologies and techniques.
• Knowledge and experience in statistical and data mining techniques:
GLM/Regression, Random Forest, Boosting, Trees, text mining, NLP, etc.
• Experience with chatbots would be bonus but not required
Responsibilities for Data Scientist/ NLP Engineer
Work with customers to identify opportunities for leveraging their data to drive business
solutions.
• Develop custom data models and algorithms to apply to data sets.
• Basic data cleaning and annotation for any incoming raw data.
• Use predictive modeling to increase and optimize customer experiences, revenue
generation, ad targeting and other business outcomes.
• Develop company A/B testing framework and test model quality.
• Deployment of ML model in production.
Qualifications for Junior Data Scientist/ NLP Engineer
• BS, MS in Computer Science, Engineering, or related discipline.
• 3+ Years of experience in Data Science/Machine Learning.
• Experience with programming language Python.
• Familiar with at least one database query language, such as SQL
• Knowledge of Text Classification & Clustering, Question Answering & Query Understanding,
Search Indexing & Fuzzy Matching.
• Excellent written and verbal communication skills for coordinating acrossteams.
• Willing to learn and master new technologies and techniques.
• Knowledge and experience in statistical and data mining techniques:
GLM/Regression, Random Forest, Boosting, Trees, text mining, NLP, etc.
• Experience with chatbots would be bonus but not required
Read more
Bengaluru (Bangalore)
1 - 2 yrs
₹7L - ₹12L / yr
Data Science
Data Analytics
SQL
Python
Datawarehousing
+2 more
Skill Set
SQL, Python, Numpy,Pandas,Knowledge of Hive and Data warehousing concept will be a plus point.
JD
- Strong analytical skills with the ability to collect, organise, analyse and interpret trends or patterns in complex data sets and provide reports & visualisations.
- Work with management to prioritise business KPIs and information needs Locate and define new process improvement opportunities.
- Technical expertise with data models, database design and development, data mining and segmentation techniques
- Proven success in a collaborative, team-oriented environment
- Working experience with geospatial data will be a plus.
SQL, Python, Numpy,Pandas,Knowledge of Hive and Data warehousing concept will be a plus point.
JD
- Strong analytical skills with the ability to collect, organise, analyse and interpret trends or patterns in complex data sets and provide reports & visualisations.
- Work with management to prioritise business KPIs and information needs Locate and define new process improvement opportunities.
- Technical expertise with data models, database design and development, data mining and segmentation techniques
- Proven success in a collaborative, team-oriented environment
- Working experience with geospatial data will be a plus.
Read more
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