Senior Data Scientist

What - How Why

% of total job

1 Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress

Utilize the existing frameworks, standards, patterns to create architectural foundation and services necessary for AI applications that scale from multi-user to enterprise class.

20%

2 Managing Data Science project life cycle from exploratory data analysis to productization ( Alpha/Beta Release) . Manage small team to collaborate with Architecture, Data Warehouse, Data Governance teams for providing analytics as service.

20%

3 Mentor team member for AI/ML development

15%

4 Verifying data quality, and/or ensuring it via data cleaning

Supervising the data acquisition process if more data is needed

Finding available datasets online that could be used for training

Defining validation strategies

Defining the preprocessing or feature engineering to be done on a given dataset

15%

5 Defining data augmentation pipelines

Training models and tuning their hyperparameters

Analyzing the errors of the model and designing strategies to overcome them

Deploying models to production

15%

2 Development of 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

15%

1. Proven experience as a Data Scientist AI/ML or similar role

2. Ability to write robust code in Python.

3. Experience in the Generative AI components like LLMs, LangChain, LlamaIndex, OpenAI, Mistral, Llama etc.

4. Experience in supervised/semi-supervised and unsupervised machine learning algorithms.

5. Experience using the cognitive APIs machine learning studios on cloud.

6. Up to speed on NLP (Summarization, Translation models, Named Entity Recognition)

7. Hands-on knowledge of image processing with deep learning (CNN, RNN, LSTM, GAN)

8. Understanding of complete AI/ML project life cycle.

9. Understanding of data structures, data modelling and software architecture.

Ability to communicate clearly and concisely and a flexible mindset to handle a quickly changing culture

Ability to work independently and/or as part of cross-domain big team

Professional and open communication to all internal and external interfaces.

Accurately report to management in a timely and effective manner.

Other Skills:

Outstanding analytical and problem-solving skills

Taking ownership of the tasks in hand and being accountable for deliverables.