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Salary
$63k – $105k per year
Location
In office (Bengaluru)
Seniority
Staff · 10+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Sep 29, 2026. First seen by Alion on Sep 29, 2026. Weekday scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Weekday is an Indian recruitment company that sources software engineers through referrals from other engineers rather than through job advertisements or agency databases. Its model pays working engineers to vouch for former colleagues they rate, turning informal knowledge about who is genuinely good into a searchable candidate pool that companies can hire from. Based in Bengaluru and backed by Y Combinator, the platform has layered AI screening and outbound sourcing on top of that referral network, and sells to startups and technology companies hiring in the Indian market.

This role is for one of Weekday’s clients

Salary range: Rs 6000000 - Rs 10000000 (ie INR 60 - 100 LPA)

Min Experience: 10+ years

Location: Bengaluru, Karnataka, India

JobType: full-time

We are looking for an experienced and highly skilled Data Scientist with 10-14 years of professional experience to join our team. The ideal candidate will have a strong background in data science, statistical modelling, machine learning, and advanced analytics, with the ability to translate complex business problems into scalable, data-driven solutions.

You will work closely with engineering, product, business, and analytics teams to identify opportunities where data can create measurable impact. The role requires strong technical depth, business understanding, and the ability to independently drive data science initiatives from problem definition through deployment and monitoring.

Requirements

Key Responsibilities

  • Lead the end-to-end development of data science solutions, from data exploration and feature engineering to model development, validation, deployment, and monitoring.
  • Design and implement statistical models, machine learning algorithms, predictive models, and optimization techniques for complex business problems.
  • Analyze large and diverse datasets to identify patterns, trends, opportunities, and actionable insights.
  • Collaborate with product, engineering, business, and leadership stakeholders to understand requirements and translate them into analytical solutions.
  • Develop robust data pipelines and workflows required for data preparation, experimentation, and model development.
  • Evaluate model performance using appropriate statistical and business metrics and continuously improve model accuracy and scalability.
  • Present complex analytical findings and recommendations clearly to both technical and non-technical stakeholders.
  • Mentor junior and mid-level data scientists and contribute to technical best practices across the team.
  • Stay updated with emerging developments in machine learning, analytics, and AI, and identify opportunities to incorporate relevant technologies into existing solutions.

Must-Have Skills

  • 10-14 years of experience in Data Science, Machine Learning, Advanced Analytics, or a closely related field.
  • Strong expertise in Python and data science libraries such as Pandas, NumPy, Scikit-learn, and similar frameworks.
  • Strong understanding of statistics, probability, hypothesis testing, regression, classification, clustering, and predictive modelling.
  • Hands-on experience developing and deploying machine learning models in production environments.
  • Strong SQL skills and experience working with large-scale datasets and databases.
  • Experience with data preprocessing, feature engineering, model validation, experimentation, and performance optimization.
  • Strong understanding of machine learning lifecycle and MLOps practices.
  • Ability to independently own complex data science projects and work effectively with cross-functional teams.
  • Excellent analytical, problem-solving, communication, and stakeholder management skills.

Good-to-Have Skills

  • AI and practical exposure to AI-driven applications.
  • Experience with Generative AI, Large Language Models (LLMs), NLP, or AI-powered products.
  • Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
  • Exposure to cloud platforms such as AWS, Azure, or Google Cloud.
  • Experience with distributed data processing technologies such as Spark.
  • Knowledge of MLOps tools and practices, including model deployment, monitoring, versioning, and CI/CD.
  • Experience working with recommendation systems, forecasting, optimization, or real-time analytics.
  • Exposure to responsible AI, model explainability, and AI governance.
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