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Premium Employer

Ciklum

Compare

3.6

based on 82 Reviews

2 Ciklum Data Scientist Jobs

Expert Data Scientist

Ciklum

3.6

based on 82 Reviews

1-2 years

Chennai

1 vacancy

Expert Data Scientist

Ciklum

posted 2mon ago

Job Role Insights

Flexible timing

Key skills for the job

Data Analysis Salesforce Logistics Supply Chain Management Project Management Mining Engineering

+ 4 more

Job Description

Description

Ciklum is looking for an Expert Data Scientist to join our team full-time in India.

We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live.

About the role:

As an Expert Data Scientist, become a part of a cross-functional development team engineering experiences of tomorrow.

Responsibilities

Development of prototype solutions, mathematical models, algorithms, machine learning techniques, and robust analytics to support analytic insights and visualization of complex data sets
Work on exploratory data analysis so you can navigate a dataset and come out with broad conclusions based on initial appraisals
Provide optimization recommendations that drive KPIs established by product, marketing, operations, PR teams, and others
Interacts with engineering teams and ensures that solutions meet customer requirements in terms of functionality, performance, availability, scalability, and reliability.
Work directly with business analysts and data engineers to understand and support their use cases
Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions
Drive innovation by exploring new experimentation methods and statistical techniques that could sharpen or speed up our product decision-making processes
Cross-train other team members on technologies being developed, while also continuously learning new technologies from other team members
Contribute to the Unit activities and community building, participate in conferences, and provide excellence in exercise and best practices
Support marketing & sales activities, customer meetings and digital services through direct support for sales opportunities & providing thought leadership & content creation for the service

Requirements

We know that sometimes, you can t tick every box. We would still love to hear from you if you think you re a good fit!

General technical requirements:

BSc, MSc, or PhD in Mathematics, Statistics, Computer Science, Engineering, Operations Research, Econometrics, or related fields
Strong knowledge of Probability Theory, Statistics, and a deep understanding of the Mathematics behind Machine Learning
Proficiency with CRISP-ML(Q) or TDSP methodologies for addressing commercial problems through data science solutions
Hands-on experience with various machine learning techniques, including but not limited to:
- Regression
- Classification
- Clustering
- Dimensionality reduction
Proficiency in Python for developing machine learning models and conducting statistical analyses
Strong understanding of data visualization tools and techniques (e.g., Python libraries such as Matplotlib, Seaborn, Plotly, etc.) and the ability to present data effectively

Specific technical requirements:

Proficiency in SQL for data processing, data manipulation, sampling, and reporting
Experience working with imbalanced datasets and applying appropriate techniques
Experience with time series data, including preprocessing, feature engineering, and forecasting
Experience with outlier detection and anomaly detection
Experience working with various data types: text, image, and video data
Familiarity with AI/ML cloud implementations (AWS, Azure, GCP) and cloud-based AI/ML services (e.g., Amazon SageMaker, Azure ML)

Domain experience:

Extensive experience working with POS systems data, including transaction records, SKU-level details, and understanding of sales cycles
Knowledge of retail operations, including merchandising, store layouts, product placements, and how these factors impact sales and customer engagement
Familiarity with inventory management data, demand planning, and supply chain logistics, including forecasting stock levels and minimizing overstock/understock scenarios
Experience working with multiple data sources from online and offline retail channels (e.g., e-commerce platforms, in-store sales) and integrating them to deliver a unified customer view
Expertise in building models to predict future demand for products based on historical sales data, seasonality, promotions, and external factors like economic trends
Strong experience in developing personalized product recommendation systems using collaborative filtering, content-based filtering, or hybrid models to enhance the customer shopping experience
Expertise in association rule mining to identify frequently bought-together items for cross-selling and upselling opportunities
Ability to develop predictive models for customer churn, identifying at-risk customers and designing targeted retention strategies
Expertise in developing personalized customer experiences, from personalized promotions to tailored product recommendations based on real-time customer interactions
Knowledge of data privacy laws and frameworks like GDPR, CCPA, and PCI DSS (Payment Card Industry Data Security Standard) to ensure the secure handling of customer data and payment information
Expertise in managing sensitive consumer data, including PII (Personally Identifiable Information), and implementing consent-based data collection practices to protect customer privacy
Familiarity with Customer Relationship Management (CRM) systems (e.g., Salesforce, HubSpot) for analyzing customer interactions and optimizing marketing and sales strategies
Ability to work with merchandisers, marketing teams, store managers, and supply chain teams to ensure that data-driven insights are actionable and aligned with business goals

Good to have skills:

Experience with MLOps, including integration of machine learning pipelines into production environments, Docker, and containerization/orchestration (e.g., Kubernetes)
Experience in deep learning development using TensorFlow or PyTorch libraries
Experience with Large Language Models (LLMs) and Generative AI applications
Advanced SQL proficiency, with experience in MS SQL Server or PostgreSQL
Familiarity with platforms like Databricks and Snowflake for data engineering and analytics
Experience working with Big Data technologies (e.g., Hadoop, Apache Spark)
Familiarity with NoSQL databases (e.g., columnar or graph databases like Cassandra, Neo4j)

Business-related requirements:

Proven experience in developing data science solutions that drive measurable business impact, with a strong track record of end-to-end project execution
Ability to effectively translate business problems into data science problems and create solutions from scratch using machine learning and statistical methods
Excellent project management and time management skills, with the ability to manage complex, detailed work and effectively communicate progress and results to stakeholders at all levels

Desirable