- Focusing on developing new concepts and user experiences through rapid prototyping and collaboration with the best-in-class research and development team.
- Reading research papers and implementing state-of-the-art techniques for computer vision
- Building and managing datasets.
- Providing Rapid experimentation, analysis, and deployment of machine/deep learning models
- Based on requirements set by the team, helping develop new and rapid prototypes
- Developing end to end products for problems related to agritech and other use cases
- Leading the deep learning team
- MS/ME/PhD in Computer Science, Computer Engineering equivalent Proficient in Python and C++, CUDA a plus
- International conference papers/Patents, Algorithm design, deep learning development, programming (Python, C/C++)
- Knowledge of multiple deep-learning frameworks, such as Caffe, TensorFlow, Theano, Torch/PyTorch
- Problem Solving: Deep learning development
- Vision, perception, control, planning algorithm development
- Track record of excellence in the machine learning / perception / control, including patents, publications to international conferences or journals.
- Communications: Good communication skills
About AI & ML Startup
Similar jobs
Who we are looking for
· A Natural Language Processing (NLP) expert with strong computer science fundamentals and experience in working with deep learning frameworks. You will be working at the cutting edge of NLP and Machine Learning.
Roles and Responsibilities
· Work as part of a distributed team to research, build and deploy Machine Learning models for NLP.
· Mentor and coach other team members
· Evaluate the performance of NLP models and ideate on how they can be improved
· Support internal and external NLP-facing APIs
· Keep up to date on current research around NLP, Machine Learning and Deep Learning
Mandatory Requirements
· Any graduation with at least 2 years of demonstrated experience as a Data Scientist.
Behavioural Skills
· Strong analytical and problem-solving capabilities.
· Proven ability to multi-task and deliver results within tight time frames
· Must have strong verbal and written communication skills
· Strong listening skills and eagerness to learn
· Strong attention to detail and the ability to work efficiently in a team as well as individually
Technical Skills
Hands-on experience with
· NLP
· Deep Learning
· Machine Learning
· Python
· Bert
Preferred Requirements
· Experience in Computer Vision is preferred
Role: Data Scientist
Industry Type: Banking
Department: Data Science & Analytics
Employment Type: Full Time, Permanent
Role Category: Data Science & Machine Learning
Job Description
Data scientist with strong background in data mining, machine learning, recommendation systems, and statistics. Should possess signature strengths of a qualified mathematician with ability to apply concepts of Mathematics, Applied Statistics, with specialization in one or more of NLP, Computer Vision, Speech, Data mining to develop models that provide effective solution.. A strong data engineering background with hands-on coding capabilities is needed to own and deliver outcomes.
A Master’s or PhD Degree in a highly quantitative field (Computer Science, Machine Learning, Operational Research, Statistics, Mathematics, etc.) or equivalent experience, 7+ years of industry experience in predictive modelling, data science and analysis, with prior experience in a ML or data scientist role and a track record of building ML or DL models.
Responsibilities and skills:
● Work with our customers to deliver a ML / DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization.
● Selecting features, building and optimizing classifiers using ML techniques ● Data mining using state-of-the-art methods, create text mining pipelines to clean & process large unstructured datasets to reveal high quality information and hidden insights using machine learning techniques
● Should be able to appreciate and work on Computer Vision problems – for example extract rich information from images to categorize and process visual data— Develop machine learning algorithms for object and image classification, Experience in using DBScan, PCA, Random Forests and Multinomial Logistic Regression to select the best features to classify objects.
OR
● Deep understanding of NLP such as fundamentals of information retrieval, deep learning approaches, transformers, attention models, text summarisation, attribute extraction, etc. Preferable experience in one or more of the following areas: recommender systems, moderation of user generated content, sentiment analysis, etc.
OR
● Speech recognition, speech to text and vice versa, understanding NLP and IR, text summarisation, statistical and deep learning approaches to text processing. Experience of having worked in these areas.
Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc. Needs to appreciate deep learning frameworks like MXNet, Caffe 2, Keras, Tensorflow
● Experience in working with GPUs to develop models, handling terabyte size datasets ● Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, mlr, mllib, Scikit-learn, caret etc - excellence in at least one of these is highly desirable ● Should be able to work hands-on in Python, R etc. Should closely collaborate & work with engineering teams to iteratively analyse data using Scala, Spark, Hadoop, Kafka, Storm etc.,
● Experience with NoSQL databases and familiarity with data visualization tools will be of great advantage
Role: Head of Analytics
Location: Bangalore (Full time)
ABOUT QRATA:
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ABOUT THE COMPANY WE ARE HIRING FOR:
Our client is offering credit card solutions for banks and financial institutions. It provides services like credit card design and onboarding, credit card authorization, payment processing, collections and dispute resolutions, credit card fraud detection, and more. They serve in the B2B space in the FinTech market segments.
POSITION OVERVIEW
We are seeking an experienced individual for the role of Head of Analytics. As the Head of Analytics, you will be responsible for driving data-driven decision-making, implementing advanced analytics strategies, and providing valuable insights to optimize our credit card business operations, sales and marketing, risk management & customer experience. Your expertise in statistical analysis, predictive modeling, and data visualization will be instrumental in driving growth and enhancing the overall performance of our credit card business.
Responsibilities:
1. Develop and implement Analytics Strategy:
o Define the analytics roadmap for the credit card business, aligning it with overall
business objectives.
o Identify key performance indicators (KPIs) and metrics to track the performance
of the credit card business.
o Collaborate with senior management and cross-functional teams to prioritize and
execute analytics initiatives. 2. Lead Data Analysis and Insights:
o Conduct in-depth analysis of credit card data, customer behavior, and market trends to identify opportunities for business growth and risk mitigation.
o Develop predictive models and algorithms to assess credit risk, customer segmentation, acquisition, retention, and upsell opportunities.
o Generate actionable insights and recommendations based on data analysis to optimize credit card product offerings, pricing, and marketing strategies.
o Regularly present findings and recommendations to senior leadership, using data visualization techniques to effectively communicate complex information.
3. Drive Data Governance and Quality:
o Oversee data governance initiatives, ensuring data accuracy, consistency, and
integrity across relevant systems and platforms.
o Collaborate with IT teams to optimize data collection, integration, and storage
processes to support advanced analytics capabilities.
o Establish and enforce data privacy and security protocols to comply with
regulatory requirements.
4. Team Leadership and Collaboration:
o Build and manage a high-performing analytics team, fostering a culture of innovation, collaboration, and continuous learning.
o Provide guidance and mentorship to the team, promoting professional growth and development.
o Collaborate with stakeholders across departments, including Marketing, Risk Management, and Finance, to align analytics initiatives with business objectives.
5. Stay Updated on Industry Trends:
o Keep abreast of emerging trends, techniques, and technologies in analytics, credit
card business, and the financial industry.
o Leverage industry best practices to drive innovation and continuous improvement
in analytics methodologies and tools.
Qualifications:
Bachelor's or master’s degree in Technology, Mathematics, Statistics, Economics, Computer Science, or a related field.
Proven experience (7+ years) in leading analytics teams in the credit card industry.
Strong expertise in statistical analysis, predictive modelling, data mining, and segmentation techniques.
Proficiency in data manipulation and analysis using programming languages such as Python, R, or SQL.
Experience with analytics tools such as SAS, SPSS, or Tableau.
Excellent leadership and team management skills, with a track record of building and developing high-performing teams.
Strong knowledge of credit card business and understanding of credit card industry dynamics, including risk management, marketing, and customer lifecycle.
Exceptional communication and presentation skills, with the ability to effectively communicate complex information to a varied audience.
Basic Qualifications:
- Five+ years experience working in a Big Data Software Development role
- Experience managing and deploying ML models in real world environments
- Bachelor's degree in Computer Science, Mathematics, Statistics, or other analytical fields
- Experience working with Python, Scala, Spark or other open-source software with data science libraries
- Experience in advanced math and statistics
- Excellent familiarity with command line linux environment
- Able to understand various data structures and common methods in data transformation
- Experience deploying machine learning models
Carsome’s Data Department is on the lookout for a Data Scientist/Data Science Lead who has a strong passion in building data powered products.
Data Science function under the Data Department has a responsibility for standardisation of methods, mentoring team of data science resources/interns, including code libraries and documentation, quality assurance of outputs, modeling techniques and statistics, leveraging a variety of technologies, open-source languages, and cloud computing platform.
You will get to lead & implement projects such as price optimization/prediction, enabling iconic personalization experiences for our customer, inventory optimization etc.
Job Descriptions
- Identifying and integrating datasets that can be leveraged through our product and work closely with data engineering team to develop data products.
- Execute analytical experiments methodically to help solve various problems and make a true impact across functions such as operations, finance, logistics, marketing.
- Identify, prioritize, and design testing opportunities that will inform algorithm enhancements.
- Devise and utilize algorithms and models to mine big data stores, perform data and error analysis to improve models and clean and validate data for uniformity and accuracy.
- Unlock insights by analyzing large amounts of complex website traffic and transactional data.
- Implement analytical models into production by collaborating with data analytics engineers.
Technical Requirements
- Expertise in model design, training, evaluation, and implementation ML Algorithm expertise K-nearest neighbors, Random Forests, Naive Bayes, Regression Models. PyTorch, TensorFlow, Keras, deep learning expertise, tSNE, gradient boosting expertise, regression implementation expertise, Python, Pyspark, SQL, R, AWS Sagemaker /personalize etc.
- Machine Learning / Data Science Certification
Experience & Education
- Bachelor’s in Engineering / Master’s in Data Science / Postgraduate Certificate in Data Science.
Responsibilities
-
Create data funnels to feed into models via web, structured and unstructured data
-
Maintain coding standards using SDLC, Git, AWS deployments etc
-
Keep abreast of developments in the field
-
Deploy models in production and monitor them
-
Documentations of processes and logic
-
Take ownership of the solution from code to deployment and performance
- Actively engage with internal business teams to understand their challenges and deliver robust, data-driven solutions.
- Work alongside global counterparts to solve data-intensive problems using standard analytical frameworks and tools.
- Be encouraged and expected to innovate and be creative in your data analysis, problem-solving, and presentation of solutions.
- Network and collaborate with a broad range of internal business units to define and deliver joint solutions.
- Work alongside customers to leverage cutting-edge technology (machine learning, streaming analytics, and ‘real’ big data) to creatively solve problems and disrupt existing business models.
In this role, we are looking for:
- A problem-solving mindset with the ability to understand business challenges and how to apply your analytics expertise to solve them.
- The unique person who can present complex mathematical solutions in a simple manner that most will understand, including customers.
- An individual excited by innovation and new technology and eager to finds ways to employ these innovations in practice.
- A team mentality, empowered by the ability to work with a diverse set of individuals.
Basic Qualifications
- A Bachelor’s degree in Data Science, Math, Statistics, Computer Science or related field with an emphasis on analytics.
- 5+ Years professional experience in a data scientist/analyst role or similar.
- Proficiency in your statistics/analytics/visualization tool of choice, but preferably in the Microsoft Azure Suite, including Azure ML Studio and PowerBI as well as R, Python, SQL.
Preferred Qualifications
- Excellent communication, organizational transformation, and leadership skills
- Demonstrated excellence in Data Science, Business Analytics and Engineering
Required skill
- Around 6- 8.5 years of experience and around 4+ years in AI / Machine learning space
- Extensive experience in designing large scale machine learning solution for the ML use case, large scale deployments and establishing continues automated improvement / retraining framework.
- Strong experience in Python and Java is required.
- Hands on experience on Scikit-learn, Pandas, NLTK
- Experience in Handling of Timeseries data and associated techniques like Prophet, LSTM
- Experience in Regression, Clustering, classification algorithms
- Extensive experience in buildings traditional Machine Learning SVM, XGBoost, Decision tree and Deep Neural Network models like RNN, Feedforward is required.
- Experience in AutoML like TPOT or other
- Must have strong hands on experience in Deep learning frameworks like Keras, TensorFlow or PyTorch
- Knowledge of Capsule Network or reinforcement learning, SageMaker is a desirable skill
- Understanding of Financial domain is desirable skill
Responsibilities
- Design and implementation of solutions for ML Use cases
- Productionize System and Maintain those
- Lead and implement data acquisition process for ML work
- Learn new methods and model quickly and utilize those in solving use cases
along with metrics to track their progress
Managing available resources such as hardware, data, and personnel so that deadlines
are met
Analysing the ML algorithms that could be used to solve a given problem and ranking
them by their success probability
Exploring and visualizing data to gain an understanding of it, then identifying
differences in data distribution that could affect performance when deploying the model
in the real world
Verifying data quality, and/or ensuring it via data cleaning
Supervising the data acquisition process if more data is needed
Defining validation strategies
Defining the pre-processing or feature engineering to be done on a given dataset
Defining data augmentation pipelines
Training models and tuning their hyper parameters
Analysing the errors of the model and designing strategies to overcome them
Deploying models to production