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Head – Data Infrastructure, Banking (10-16 yrs)

Kunal Jain
Kunal Jain Last Updated : 21 Mar, 2014
2 min read

Looking for a role within BFSI domain? If you have got 10+ years of experience in Data Analytics and are ready to lead a large team (~40 people) with people managers reporting to you, this role might be for you.

 

Designation – Head, Data Infrastructure

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Location – Mumbai, India

About employer – Banking giant.

Responsibility –

  • Lead the 40 member Data Infrastructure team including 10 in-house and 30 outsourced resources
  • Build the data foundation needed for the rest of the team to perform their analytics and reporting functions
  • Acquire data from new sources, more granular data from existing sources and organize the same in to well optimized datamarts.
  • Interact with other members of the BIU as well as with internal customers across the bank to understand their needs and proactively address them.
  • Strategize and direct the data and hardware architecture changes needed to march the bank’s BIU in to the big data world.

 

Ideal profile –

  1. 9+ years of relevant experience in the technology for analytics, business intelligence, information management, data warehousing or datamarts.
  2. Well-versed in analytic tools and technologies like SAS (or related software), SQL, SAP BO, rule engines, informatica ETL etc
  3. Solid understanding of datawarehousing concepts, how to structure datamarts and optimize them for querying and / or analytic purposes
  4. Track record of increasing responsibility and success in leadership roles
  5. Excellent communication skills. Ability to interact with non-technical stakeholders and solve their data related problems
  6. Great leadership skills. Very proactive. Interacts with other business leaders to both learn as well as influence. Partnership approach and collaborative attitude
  7. Tremendous drive towards results. Ability to persist against obstacles and get things done
  8. Displays great ownership of problems and results. Can do and positive attitude
  9. Bachelor’s degree in a strong technical discipline

 

Skills required:

  • Building Data Infrastructure, Data Quality & Governance
  • Proficient in Enterprise Data Infrastructure, DataWarehouse and ETL
  • Informatica & SAS (mandatory)

You can find more details about the job and apply here: http://goo.gl/5qTWVp

Kunal Jain
Kunal Jain

Kunal Jain is the Founder and CEO of Analytics Vidhya, one of the world's leading communities of Al professionals. With over 17 years of experience in the field, Kunal has been instrumental in shaping the global Al landscape. His expertise spans diverse markets, from developed economies like the UK to emerging ones like India, where he has successfully led and delivered complex data-driven solutions. As a recognized thought leader, Kunal has empowered countless individuals to realize their Al ambitions through his visionary approach to Al education and community building. Before founding Analytics Vidhya, Kunal earned both his undergraduate and postgraduate degrees from IIT Bombay and held key roles at Capital One and Aviva Life Insurance across multiple geographies. His passion lies at the intersection of analytics, Al, and fostering a thriving community of data science professionals.

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Flash Card

What are neural networks and how do they function?

Hey there! So, neural networks are like these cool computer models that take a leaf out of our brain's book. They're made up of a bunch of interconnected nodes, or neurons if you will, and they're organized into layers. Here's how they do their thing:

  • Input Layer: This is where the network gets its first taste of data.
  • Hidden Layers: These guys roll up their sleeves and do the heavy lifting by crunching numbers and transforming the input data.
  • Output Layer: Finally, this layer spits out the result.
Each of these neurons uses something called an activation function to add a bit of flair, allowing the network to learn all sorts of complex patterns. That's why they're super handy for things like recognizing images and speech—they're great at spotting the complicated stuff!

What are neural networks and how do they function?

Quiz

What are neural networks and how do they function?

Flash Card

Why is the architecture of neural networks important?

Alright, let's talk about why the architecture of these networks is such a big deal. Think of it as the blueprint that decides how well the network can learn from the data. It covers everything from how the layers and neurons are set up to which activation functions to use. Here's why you should care:

  • Input Layer: This sets the stage for how data enters the network.
  • Hidden Layers: The number and size of these layers are like the network's brainpower—they determine how well it can learn those tricky patterns.
  • Output Layer: Needs to match up with what you're trying to predict or classify.
The architecture also plays a big role in how the network handles tricky stuff like non-linear data and how efficiently it learns. So, getting this right is key!

Why is the architecture of neural networks important?

Quiz

Why is the architecture of a neural network important?

Flash Card

How do neural networks learn from data?

So, neural networks have this thing called training where they learn from data. It's kind of like teaching them to get better at their job. Here's how it works:

  • Forward Propagation: This is when data travels through the network to make predictions.
  • Loss Calculation: Then, they figure out how off they were by comparing predicted results with the real deal.
  • Backward Propagation: Finally, they tweak the connections between neurons to get better results next time.
Once they're trained, these networks can make predictions on new stuff they've never seen before, using all the patterns they've picked up during training. Pretty neat, huh?

How do neural networks learn from data?

Quiz

How do neural networks learn from data?

Flash Card

What are the different types of neural networks and their applications?

Now, let's chat about the different flavors of neural networks and what they're good at:

  • Perceptrons: The basic ones, great for simple yes-or-no questions.
  • ANNs (Artificial Neural Networks): The all-rounders, can handle a bunch of different tasks.
  • CNNs (Convolutional Neural Networks): These are the image gurus, perfect for spotting patterns in pictures.
  • RNNs (Recurrent Neural Networks): Love working with sequences, like time series or text.
  • LSTMs (Long Short-Term Memory networks): A special kind of RNN that remembers stuff for longer.
  • RBF Networks (Radial Basis Function Networks): Great for functions and pattern recognition.
Each one has its own superpowers and is chosen based on what you need to tackle in the world of deep learning.

What are the different types of neural networks and their applications?

Quiz

Which type of neural network is ideal for image data due to its ability to capture spatial hierarchies?

Flash Card

What advantages does deep learning have over traditional machine learning?

Deep learning is like the superhero of machine learning, and here's why:

  • Automated Feature Extraction: These models can dig out important features from raw data all by themselves, so you don't have to do it manually.
  • Handling Complex Data: They can handle messy data like images and text, making them perfect for tasks like image classification and understanding language.
  • Scalability: The more data you throw at them, the better they get, which is awesome for big datasets.
All these perks make deep learning a go-to choice for modern AI challenges.

What advantages does deep learning have over traditional machine learning?

Quiz

What is one key advantage of deep learning over traditional machine learning?

Flash Card

What challenges do neural networks face during training?

Training these networks isn't all sunshine and rainbows—there are some bumps along the way:

  • Non-linear Separability: Picking the right architecture to handle tricky data distributions.
  • Architecture Selection: Finding the sweet spot with layers and neurons to balance complexity and performance.
  • Vanishing/Exploding Gradients: Sometimes gradients get too small or too big during training, making learning tough. But don't worry, techniques like normalization and advanced optimizers can help fix this.
Tackling these challenges is key to building strong and accurate neural networks.

What challenges do neural networks face during training?

Quiz

Which challenge involves gradients becoming too small or too large during neural network training?

Flash Card

How do different neural networks compare in handling various data types?

Different networks have their own special talents for handling different kinds of data:

  • CNNs: Rockstars at processing images because they can spot spatial patterns and hierarchies.
  • RNNs: Built for handling sequences like time series and text, thanks to their knack for keeping track of time-based dependencies.
  • LSTMs: A special kind of RNN that's great for long sequences where remembering stuff for a long time is crucial.
Each type has its strengths and weaknesses, so picking the right one depends on what your data looks like and what you're trying to achieve.

How do different neural networks compare in handling various data types?

Quiz

Which type of neural network is best suited for sequential data like time series or text?

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Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
What is Recurrent Neural Networks (RNN)?
What is Recurrent Neural Networks (RNN)?