Data Scientist 2

Role:
DS II

Location:
Bengaluru (Hybrid)

Experience:
2-4 years

Akaike Technologies, Greater Bengaluru Area

Company Overview:

Akaike Technologies is a dynamic and innovative AI-driven company dedicated to building impactful solutions across various domains. Our mission is to empower businesses by harnessing the power of data and AI to drive growth, efficiency, and value. We foster a culture of collaboration, creativity, and continuous learning, where every team member is encouraged to take initiative and contribute to groundbreaking projects. We value diversity, integrity, and a strong commitment to excellence in all our endeavors
.

Job Description:

We are seeking an innovative and driven Data Scientist to join our dynamic team. This role is at the heart of our mission to build intelligent systems that solve real-world business problems. You will be instrumental in designing and deploying cutting-edge solutions that leverage
Generative AI, agentic systems, deep learning, and classical machine learning
. The ideal candidate is a hands-on builder with a strong foundation in data analysis and software engineering, who thrives in a fast-paced environment and is passionate about pushing the boundaries of what's possible with AI. Experience in the
pharmaceutical or life sciences domain
is highly valued.

Key Responsibilities:

Design & Build GenAI Systems:
Lead the design, development, and deployment of robust Generative AI solutions, including
agentic systems
and
Retrieval-Augmented Generation (RAG)
pipelines to tackle complex business challenges.

Develop Intelligent Agents:
Create sophisticated agents capable of reasoning and executing tasks over large-scale
structured data (e.g., databases, APIs)
and unstructured data.

Ensure System Reliability:
Establish and implement rigorous frameworks for
evaluating, testing, and ensuring the reliability, safety, and accuracy
of LLM-based systems.

End-to-End Model Ownership:
Apply a wide range of machine learning and deep learning techniques (e.g., forecasting, CLV, recommendation systems, NLP) and own the entire model lifecycle—from rapid prototyping to deploying scalable, low-latency solutions using
Docker and AWS services
.

Large-Scale Data Mastery:
Process and analyze massive datasets (billions of records) using distributed computing frameworks like
PySpark
to extract actionable insights and engineer impactful features.

Experimentation & Communication:
Design and conduct experiments to validate hypotheses, perform insightful EDA, and effectively communicate solution outlines and results to stakeholders and team members.

Mentorship & Collaboration:
Mentor junior team members and act as a bridge between business problems and data science, working closely with cross-functional engineering and product teams.

Core Qualifications & Skills

Experience:
2-4 years of hands-on experience in a data science role, building and deploying machine learning models in a production environment.

Generative AI Proficiency:
Demonstrated experience building solutions using Large Language Models (LLMs), with specific expertise in
RAG architectures
and
agentic frameworks
(e.g., LangChain, Langgraph, or similar).

ML & Deep Learning:
Strong foundation in classical machine learning algorithms and deep learning architectures (ANN, CNNs, LSTMs, Transformers).

Programming & Data Analysis:
High proficiency in
Python
and its data science ecosystem (
Pandas, NumPy, Scikit-learn
). Excellent SQL skills are a must.

Big Data Technologies:
Proven experience with distributed data processing frameworks, particularly
PySpark
.

Problem-Solving:
Exceptional analytical, logical reasoning, and problem-solving skills with a data-driven approach.

Engineering & Deployment:
Solid understanding of system design concepts and MLOps principles, including containerization (
Docker
) and cloud services (
AWS stack
: S3, Lambda, ECR, Step Functions, etc.).

Data Analysis & Machine Learning
:

Utilize Python libraries like NumPy, Pandas, and Dask, Pyspark for data processing and analysis.

Apply ML/DL libraries like Scikit-learn, TensorFlow/Keras, PyTorch for developing and deploying models.

Traditional NLP Expertise
: Work on advanced NLP techniques including Transformers (BERT, T5, GPT), Word2Vec, NER, topic modeling, and contrastive learning.

Good understanding of Python Ecosystem and implementing research papers

Collaboration & Communication
: Work closely with cross-functional teams and clients to deliver impactful solutions.

Fast paced development and Rapid prototyping environment.

Preferred Qualifications:

Domain expertise in
Pharma or Life Sciences
, with an understanding of claims data, commercial analytics.

Hands-on experience with
fine-tuning
open-source LLMs (e.g., Llama, Mistral) for specific tasks.

Deep understanding of the latest developments in agentic systems, including
MCP
and multi-agent frameworks (e.g., AutoGen, CrewAI).

Experience with advanced ML techniques such as Positive-Unlabeled (PU) learning, representation learning, and advanced model explainability.

Prior experience in building domain-specific models like
Marketing Mix Models (MMM)
, demand forecasting, or multi-dimensional time series analysis.

Contributions to open-source AI/ML projects or publications in relevant fields.

Benefits and Perks

● Competitive ESOP grants

● Working with fortune 500 companies and world class teams

● Publishing papers and attending conferences

● Opportunity to go to networking events, conferences, and seminars

● Visibility on all the functions at Akaike including sales, pre-sales, lead generation, marketing, and hiring