SDE 1/SDE 2 - Data Scientist

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SDE 1/SDE 2 - Data Scientist

Fynd

Ahmedabad INR 6 - 8 LPA Experience : 1 - 3 YRS. Openings: 2

Details:

Fynd is India’s largest omnichannel platform and multi-platform tech company with expertise in retail tech and products in AI, ML, ample data ops, gaming+crypto, image editing and learning space.
 
We were founded in 2012 by 3 IIT Bombay alumni: Farooq Adam, Harsh Shah and Sreeraman MG.
 
We are headquartered in Mumbai and are a company of more than 750 people. Fynd has 1000+ brands under management, more than 10k stores and servicing 23k + pin codes.
 
Everybody these days is making products, and everyone is hiring technology ninjas! We aren’t looking for ninjas, samurais or warriors; we are looking for YOU:
 
* If you know the difference between worKING and work-eh-ing
* You know when to stop, unwind, and take a break!
* Unafraid to ask for help & unashamed to ask again
* If you are good at what you do & acknowledge when you mess up (We all mess up sometimes)
* You learn, execute, learn again and talk about the insights you gained
 
Then YOU are the chosen one that we are looking for….
 
What do we offer?
 
Growth
Our company has grown more than 2x in people, 3x in products and 4x in-office presence, in the past year alone. We are looking at new product lines, international markets and brilliant people to grow further.
We teach, groom and nurture our people to become leaders. You get to grow with a company that is growing exponentially.
Flex University: We help you upskill by organising in-house courses on essential subjects
Learning Wallet: You can also do an external course to upskill and grow; we reimburse it.
 
Other Perks
A lucrative Retention Bonus
ESOP is for people who go above and beyond
Best in the market salary and appraisal
 
Culture
We listen to people: Make big + small changes wherever required.
Go on Workcations all year long.
Host weekly, quarterly and annual events/parties.
 
Wellness
Mediclaim policy for you + parents + spouse + kids
Experienced therapist for better mental health
Coaching to improve productivity & work-life balance 
 
We work five days from the office, and we make sure people have everything they need:-
* Free meals
* Shuttle service
* Snacks, goodies & a lot fun culture

Min. Qualification:

Some Specific Requirements

    • Efficient in Python Programming language, Pandas, SQL
    • Having knowledge of Statistics, Machine Learning - Regression, Classification and Clustering, Deep Learning - RNN, CNN, Transformers
    • Experience working with End to End Machine Learning project Life cycle
    • Experience working over one of the ML Frameworks - SKLearn, XGBoost, PyCaret and Deep Learning Frameworks - Tensorflow, PyTorch
    • Experience working over ML pipelines for Model training and Model inferencing
    • Able to work over data profiling to understand and debug the inefficiency of algorithms
    • Able to work over datasets to identify insights by performing statistical techniques with EDA
    • Prior experience in building Recommender System and searching for eCommerce domains would be a plus

Skills Required:

Python, Pandas, SQL, Statistics, Machine Learning

Roles:

What will you do at Fynd?

    • Work with a team of Business stakeholders, Data Engineers and Data Scientists.
    • Guide the Data Engineering team to extract datasets and apply relevant transformations for specific problem statements
    • Work independently to perform Data profiling, Exploratory Data Analysis and Data completeness checks to use it well with AI/ML algorithms.
    • Work with project stack holders to understand the business problems and apply feature engineering to prepare the relevant data signals for model training and improvement.
    • Implement robust AI/ML pipeline which can perform operations like - Data Cleaning, Data Transformation, Feature Engineering, Model Training at scale, Evaluate and Improvement and Serving
    • Work with ML engineers to deploy the AI/ML models to scalable production systems with the Kubernetes platform.
    • Monitor and improve the models periodically.

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