About us:
Siply helps underserved masses inculcate savings behaviour to build a more productive and financially secure country. Our Vision is to build the Digital Bank for 40 Crore Underserved Indians.
Company Link - https://siply.in/">https://siply.in/
About the Role:
We are looking for a Product Designer who can demonstrate high agency and open to iterate rapidly. Should deeply understand customer problems and take on a multitude of roles that facilitate the creation of a solution.
Responsibilities:
User Research:
- Advocate the customer.
- Gather insights and feedback.
- Evaluate user experience to affect strategy and roadmaps.
- Evangelise research in the organization.
Interaction Design :
- Solve problems through existing design patterns or facilitate problem-solving through new patterns.
- Create mockups and flows for the user interface.
- Document how these new patterns work.
Visual Design :
- Mastery and application of color, space, typography, iconography, illustration, and UX laws.
- Strong “taste” and aesthetics.
- Understanding of brand across different platforms.
Prototyping :
- Use prototyping tools like code, after effects, Framer, Origami, InVision.
- Implement one’s own flows and interactions.
- Test ideas in high fidelity.
Qualification:
- 3-4 years of experience in a related field along with a strong portfolio demonstrating past work experience in UI/UX design.
- Proficiency in using software such as Sketch, Principle, Figma, Zeplin, Marvel, Invision, Framer
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Managing available resources such as hardware, data, and personnel so that deadlines
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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
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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