Data Scientist Jobs in Pune.
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The Role
Lead the maturation of Securly's content classification system — building the ML infrastructure that determines, at scale, whether web content is appropriate for K-12 students, and establishing the rigorous evaluation framework that product and leadership teams depend on.
This is applied ML with direct student safety impact — not research. You will lead a significant uplift of Securly's classification models: refactoring binary models to proper multiclass classification, building labeled evaluation datasets, and producing standardized model cards with per-category precision, recall, F1, and confusion matrix analysis.
At L5, you are the technical leader of the data science function for content safety. You will define the evaluation methodology the team follows, set the standard for what a model card must contain before a model ships, mentor the team on applied ML rigor, and serve as the interface between data science and engineering on production integration constraints.
What It Means to Be L5 at Securly
L5 at Securly is a Staff Engineer. You are the technical owner, not just an implementer.
- Drive technical direction for your initiative end-to-end: from architecture to production, with minimal oversight from your engineering manager.
- Identify and resolve ambiguity in requirements, system boundaries, and design tradeoffs without waiting for a fully-formed spec.
- Mentor L3/L4 engineers on the team: code reviews, design feedback, pairing, and raising the bar for what production-quality work looks like.
- Partner with your L6 technical lead and the Distinguished Engineer on architectural decisions, surfacing tradeoffs clearly rather than deferring them upward.
- Contribute to cross-team engineering standards: you are expected to influence practices beyond your immediate squad.
- Translate technical context into clear written artifacts that non-engineers (PM, Support, Leadership) can act on.
- Participate in on-call rotation and own the full incident lifecycle for your system: detection, diagnosis, resolution, and retrospective.
What You'll Do
- Define the evaluation methodology for content classification at Securly: establish what a model card must contain and hold every model release to that standard before it ships.
- Lead the multiclass refactor of Securly's content classification models: redesign binary models to handle multi-label, multi-class content categories (Adult Content, Violence, Self-Harm, Social Media, and others).
- Build and maintain labeled evaluation datasets with robust annotation workflows; address class imbalance and label noise systematically; document dataset curation decisions in a versioned data card.
- Connect offline evaluation to production monitoring — surface classification drift and error patterns before they become customer-facing issues.
- Investigate and resolve misclassification errors: false positives (over-blocking) and false negatives (under-blocking); produce written root cause analyses.
- Build and maintain training data pipelines: ingestion, cleaning, labeling, and versioning at scale.
- Mentor the existing AI team on evaluation methodology, model development practices, and data science communication rigor.
- Communicate precision/recall tradeoffs to product managers and engineers; produce executive-level summaries of classification quality for leadership.
- Collaborate with engineering to integrate model outputs into the production filtering stack with appropriate latency and reliability constraints.
- Research and prototype improvements: feature representations, model architectures, active learning for label efficiency, domain adaptation for emerging content categories.
Skills & Requirements
Must-Have
- Machine learning — multi-label/multi-class classification, model evaluation methodology, handling class imbalance, feature engineering for text and URL data. 5+ years in applied ML roles.
- Python (ML stack) — production-quality code: scikit-learn, PyTorch or TensorFlow, pandas, numpy. Notebooks for exploration; production-grade pipelines for delivery.
- Text / NLP feature engineering — URL tokenization, domain analysis, HTML content features, TF-IDF or embedding-based representations for web content classification.
- ML evaluation rigor — precision/recall tradeoffs, confusion matrix analysis, offline vs. online evaluation, A/B testing, reproducible model cards. At L5, you define the evaluation standard.
- Data engineering for ML — training data pipelines, data versioning, handling noisy and partially labeled datasets, annotation workflow design.
- Technical communication and stakeholder influence — ability to present quantitative model quality findings to both engineering and non-technical leadership.
Strongly Preferred
- Large-scale classification in production — shipping models with latency and throughput constraints; understanding the gap between offline eval metrics and live production behavior.
- Active learning / annotation workflows — strategies for efficient label acquisition on large, imbalanced datasets.
- Cloud ML infrastructure — AWS SageMaker, GCP Vertex AI, or equivalent for training pipelines, experiment tracking, and model deployment.
Nice to Have
- Web content / URL classification domain — prior work on web categorization, safe browsing, or content policy systems.
- K-12 / CIPA compliance — understanding of regulated content categories and compliance requirements around false negative rates.
- LLM-based classification — zero-shot or few-shot content classification for emerging categories without labeled training data.
- Graph / network features — domain co-occurrence, DNS graph signals, or network-based features for domain classification at scale.
Who You Are
- You have shipped ML models to production and lived with the consequences — you know what model drift looks like and how to catch it before it becomes a customer issue.
- You treat evaluation as a first-class engineering artifact. A model without a model card is not finished — and you set and enforce that standard for the team.
- You define the methodology, not just apply it. You produce the evaluation framework that other data scientists use, and you hold them to it.
- You can communicate precision/recall tradeoffs to a product manager and to a senior engineer in the same conversation, calibrated to each audience.
- You are energized by problems with real stakes: a false negative in Self-Harm classification is not an acceptable error rate.
- You mentor by example and by expectation: your code, your analysis, and your documentation set the standard.
About Securly
Securly processes over 1.1 billion requests per day and 54 TB of data daily, protecting more than 20 million students across 20,000+ schools globally. Since pioneering the first cloud-based web filter for K-12 in 2013, Securly has built one of the most trusted, high-scale platforms for student safety, wellness, and engagement. By turning data into meaningful, actionable intelligence, Securly enables schools to identify risk earlier, reduce harmful incidents, and strengthen student support.
We are proud to be consistently recognized as a Top Place to Work, named a Top 40 Most Used EdTech platform, and included on the GSV 150 list as one of the most transformational growth companies in digital learning and workforce skills.
Benefits
- Comprehensive Health Insurance (employee, parents, spouse, children)
- Accidental & Term Life Insurance
- Learning & Development reimbursement
- Paid Time Off
- Public Holidays (10+ per year)
- Retirement Benefits (EPF & gratuity)
- Parental Leave (as per statutory norms)
Securly is an Equal Opportunity Employer committed to inclusion, fairness, and respect. We welcome applicants from all backgrounds, identities, and experiences. #LI-REMOTE #LI-DO1
Ready to apply?
Apply to Securly
The Role
The Senior Data Scientist will lead the maturation of Securly's content classification system — building the ML infrastructure that determines, at scale, whether web content is appropriate for K-12 students, and establishing the rigorous evaluation framework that product and leadership teams depend on.
This is applied ML with direct student safety impact — not research. You will lead a significant uplift of Securly's classification models: refactoring binary models to proper multiclass classification, building labeled evaluation datasets, and producing standardized model cards with per-category precision, recall, F1, and confusion matrix analysis.
At L5, you are the technical leader of the data science function for content safety. You will define the evaluation methodology the team follows, set the standard for what a model card must contain before a model ships, mentor the team on applied ML rigor, and serve as the interface between data science and engineering on production integration constraints.
What It Means to Be L5 at Securly
L5 at Securly is a Staff Engineer. You are the technical owner, not just an implementer.
- Drive technical direction for your initiative end-to-end: from architecture to production, with minimal oversight from your engineering manager.
- Identify and resolve ambiguity in requirements, system boundaries, and design tradeoffs without waiting for a fully-formed spec.
- Mentor L3/L4 engineers on the team: code reviews, design feedback, pairing, and raising the bar for what production-quality work looks like.
- Partner with your L6 technical lead and the Distinguished Engineer on architectural decisions, surfacing tradeoffs clearly rather than deferring them upward.
- Contribute to cross-team engineering standards: you are expected to influence practices beyond your immediate squad.
- Translate technical context into clear written artifacts that non-engineers (PM, Support, Leadership) can act on.
- Participate in on-call rotation and own the full incident lifecycle for your system: detection, diagnosis, resolution, and retrospective.
What You'll Do
- Define the evaluation methodology for content classification at Securly: establish what a model card must contain and hold every model release to that standard before it ships.
- Lead the multiclass refactor of Securly's content classification models: redesign binary models to handle multi-label, multi-class content categories (Adult Content, Violence, Self-Harm, Social Media, and others).
- Build and maintain labeled evaluation datasets with robust annotation workflows; address class imbalance and label noise systematically; document dataset curation decisions in a versioned data card.
- Connect offline evaluation to production monitoring — surface classification drift and error patterns before they become customer-facing issues.
- Investigate and resolve misclassification errors: false positives (over-blocking) and false negatives (under-blocking); produce written root cause analyses.
- Build and maintain training data pipelines: ingestion, cleaning, labeling, and versioning at scale.
- Mentor the existing AI team on evaluation methodology, model development practices, and data science communication rigor.
- Communicate precision/recall tradeoffs to product managers and engineers; produce executive-level summaries of classification quality for leadership.
- Collaborate with engineering to integrate model outputs into the production filtering stack with appropriate latency and reliability constraints.
- Research and prototype improvements: feature representations, model architectures, active learning for label efficiency, domain adaptation for emerging content categories.
Skills & Requirements
Must-Have
- Machine learning — multi-label/multi-class classification, model evaluation methodology, handling class imbalance, feature engineering for text and URL data. 5+ years in applied ML roles.
- Python (ML stack) — production-quality code: scikit-learn, PyTorch or TensorFlow, pandas, numpy. Notebooks for exploration; production-grade pipelines for delivery.
- Text / NLP feature engineering — URL tokenization, domain analysis, HTML content features, TF-IDF or embedding-based representations for web content classification.
- ML evaluation rigor — precision/recall tradeoffs, confusion matrix analysis, offline vs. online evaluation, A/B testing, reproducible model cards. At L5, you define the evaluation standard.
- Data engineering for ML — training data pipelines, data versioning, handling noisy and partially labeled datasets, annotation workflow design.
- Technical communication and stakeholder influence — ability to present quantitative model quality findings to both engineering and non-technical leadership.
Strongly Preferred
- Large-scale classification in production — shipping models with latency and throughput constraints; understanding the gap between offline eval metrics and live production behavior.
- Active learning / annotation workflows — strategies for efficient label acquisition on large, imbalanced datasets.
- Cloud ML infrastructure — AWS SageMaker, GCP Vertex AI, or equivalent for training pipelines, experiment tracking, and model deployment.
Nice to Have
- Web content / URL classification domain — prior work on web categorization, safe browsing, or content policy systems.
- K-12 / CIPA compliance — understanding of regulated content categories and compliance requirements around false negative rates.
- LLM-based classification — zero-shot or few-shot content classification for emerging categories without labeled training data.
- Graph / network features — domain co-occurrence, DNS graph signals, or network-based features for domain classification at scale.
Who You Are
- You have shipped ML models to production and lived with the consequences — you know what model drift looks like and how to catch it before it becomes a customer issue.
- You treat evaluation as a first-class engineering artifact. A model without a model card is not finished — and you set and enforce that standard for the team.
- You define the methodology, not just apply it. You produce the evaluation framework that other data scientists use, and you hold them to it.
- You can communicate precision/recall tradeoffs to a product manager and to a senior engineer in the same conversation, calibrated to each audience.
- You are energized by problems with real stakes: a false negative in Self-Harm classification is not an acceptable error rate.
- You mentor by example and by expectation: your code, your analysis, and your documentation set the standard.
About Securly
Securly processes over 1.1 billion requests per day and 54 TB of data daily, protecting more than 20 million students across 20,000+ schools globally. Since pioneering the first cloud-based web filter for K-12 in 2013, Securly has built one of the most trusted, high-scale platforms for student safety, wellness, and engagement. By turning data into meaningful, actionable intelligence, Securly enables schools to identify risk earlier, reduce harmful incidents, and strengthen student support.
We are proud to be consistently recognized as a Top Place to Work, named a Top 40 Most Used EdTech platform, and included on the GSV 150 list as one of the most transformational growth companies in digital learning and workforce skills.
Benefits
- Comprehensive Health Insurance (employee, parents, spouse, children)
- Accidental & Term Life Insurance
- Learning & Development reimbursement
- Paid Time Off
- Public Holidays (10+ per year)
- Retirement Benefits (EPF & gratuity)
- Parental Leave (as per statutory norms)
Securly is an Equal Opportunity Employer committed to inclusion, fairness, and respect. We welcome applicants from all backgrounds, identities, and experiences. #LI-REMOTE #LI-DO1
Ready to apply?
Apply to Securly
Applied Data Scientist - Research
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About HG Insights
HG Insights is the pioneer of Revenue Growth Intelligence. For more than a decade, we have delivered comprehensive, AI-driven datasets on B2B buyers, technology adoption, IT spend, and buyer intent, sourced from billions of data points. Today, we are a trusted partner to Fortune 500 technology companies, hyperscalers, and innovative B2B vendors seeking precise go-to-market analytics and decision-making.
Through an evolving suite of AI agents that incorporate first-party data and buyer signals, HG Insights enables AI-powered GTM automation across sales, marketing, RevOps, and data analytics teams, modernizing GTM execution from strategy through activation.
Role Overview
The Applied Data Scientist - Research is a collaborative analytical partner to the Head of Data Science, contributing to the design and validation of GTM insights that power the Contextual Intelligence initiative.
You will co-develop insight logic, selecting signals, designing scoring frameworks, prototyping models in Python, and validating outputs. You will also contribute to the production-ready briefs that are implemented in the data production pipeline by the engineering team.
This role sits at the intersection of statistical modeling, structured data analysis, and applied AI. You are comfortable reasoning about how to measure something rigorously, how entities and relationships in a knowledge graph can be leveraged, and how to use LLMs as a practical tool in the insight development workflow, not as a subject of research, but as part of the toolkit.
What You Will Do
Insight & Model Development
- Co-develop scoring frameworks and metrics models, contributing to signal selection, weighting logic, and model structure across a range of GTM insight types (acquisition,expansion, retention, strategic)
- Prototype insight logic in Python notebooks: assembling features from HG's structured data assets, implementing model components, and stress-testing outputs.
- Design and run validation experiments to confirm that insight outputs are directionally correct, well-calibrated, and meaningful across the full vendor universe
- Contribute to ontology and entity design, thinking through how vendors, products, companies, and relationships should be structured to support a given insight, informed by a conceptual understanding of the knowledge graph schema
Production Brief Development
- Translate insight designs into clear, implementation-ready production briefs
- Document model specifications precisely: component definitions, feature engineering, aggregation logic, edge case handling, and expected output distributions
- Participate in handoff reviews with the production function, answering implementation questions and refining specs based on feasibility feedback
Insight Research & Discovery
- Contribute to the prioritized insights catalog, researching new insight ideas, assessing data availability, and framing feasibility
- Stay current on GTM data science approaches, competitive intelligence methodologies, and relevant analytical techniques that could expand the insight library
What We're Looking For
Core Skills
- Statistical modeling depth: Ability to design and implement a range of scoring and metrics models from first principles; comfortable with component weighting, normalization, signed rate-of-change metrics, composite aggregation, and distribution analysis; knows when a technique is appropriate and why
- Python for analytical prototyping: Strong notebook-based Python for data manipulation, feature construction, model prototyping, and output validation; pandas, NumPy, and Scikit are daily
- SQL: Proficient in querying structured data at scale; used for signal extraction, feature derivation, and validation checks across large vendor and company datasets
- Analytical rigor & validation thinking: Ability to critically evaluate whether a model is measuring what it claims to measure; designs validation experiments, checks edge cases, and flags when outputs don't pass a sanity check
- Clear technical communication: Able to translate analytical logic into precise written specifications; the production brief is a key deliverable
Applied AI & Graph Literacy
- LLM API usage: Hands-on experience using Claude, GPT, or equivalent APIs as a practical tool; can design effective prompts, integrate LLM steps into an analytical workflow, and evaluate output quality critically
- Knowledge graph concepts: Conceptual understanding of how entities, relationships, and properties are structured in a graph; able to reason about how graph-derived features (e.g., vendor-product-company traversals) should inform insight design, without necessarily writing production Cypher
Nice to Have
- GTM/Management Consulting, or IT Research experience, familiarity with concepts like install base, intent signals, competitive intelligence, and market analysis. Experience writing Cypher or querying graph-structured data directly
- Experience working collaboratively with engineering, product and GTM teams
- Experience in a B2B SaaS or data products environment
Tools & Environment
Primary
- Python (pandas, NumPy, scipy, Jupyter)
- SQL
- LLM APIs (Claude, GPT)
- Git and version control
Working Knowledge
- Databricks
- Cloud storage
- Knowledge graph concepts
Ready to apply?
Apply to HG Insights
At NiCE, we don’t limit our challenges. We challenge our limits. Always. We’re ambitious. We’re game changers. And we play to win. We set the highest standards and execute beyond them. And if you’re like us, we can offer you the ultimate career opportunity that will light a fire within you.
So, what’s the role all about?
At Actimize, we stop bad people from doing bad things. As part of our mission to enhance financial crime detection and prevention, we are building a next-generation Fin-Crime Investigation & Detection system powered by external data integrations and holistic insights. We are seeking a dynamic individual with high energy and drive to join our dynamic and growing X-Sight Monitor DS and AI team as a Specialist Data Analyst (AML).
How will you make an impact?
NiCE Actimize is the largest and broadest provider of financial crime, risk and compliance solutions for regional and global financial institutions & has been consistently ranked as number one in the space
At NiCE Actimize, we recognize that all employee’s contributions are integral to our company’s growth and success. To find and acquire the best and brightest talent around the globe, we offer a challenging work environment, competitive compensation, and benefits, and rewarding career opportunities. Come share, grow and learn with us – you’ll be challenged, you’ll have fun and you’ll be part of a fast growing, highly respected organization.
This new SaaS platform will enable our customers (some of the biggest financial institutes around the world) to create solutions on the platform to fight financial crime.
Have you got what it takes?
- Overall, 8–12 years relevant three years of strong domain experience in Anti-Money Laundering
- Strong understanding of financial crime regulations, compliance, and risk management with a focus on AML Detection.
- Experience with SQL (Python – advantage).
- Experience in Product Development or Product Management advantage.
- Knowledge or familiarity with Cloud technologies advantage.
- Serve as an SME to support product requirements and domain knowledge. Analyse data and available information to deliver analytical features and logic for AML
- Strong problem-solving and analytical capabilities.
- Excellent verbal and written communication skills
- Ability to engage both technical and non-technical stakeholders effectively.
What’s in it for you?
Join an ever-growing, market disrupting, global company where the teams – comprised of the best of the best – work in a fast-paced, collaborative, and creative environment! As the market leader, every day at NiCE is a chance to learn and grow, and there are endless internal career opportunities across multiple roles, disciplines, domains, and locations. If you are passionate, innovative, and excited to constantly raise the bar, you may just be our next NiCEr!
Enjoy NiCE-FLEX!
At NiCE, we work according to the NiCE-FLEX hybrid model, which enables maximum flexibility: 2 days working from the office and 3 days of remote work, each week. Naturally, office days focus on face-to-face meetings, where teamwork and collaborative thinking generate innovation, new ideas, and a vibrant, interactive atmosphere.
Requisition ID: 10241
Reporting into: Group Manager
Role Type: Individual Contributor
About NiCE
NICE Ltd. (NASDAQ: NICE) software products are used by 25,000+ global businesses, including 85 of the Fortune 100 corporations, to deliver extraordinary customer experiences, fight financial crime and ensure public safety. Every day, NiCE software manages more than 120 million customer interactions and monitors 3+ billion financial transactions.
Known as an innovation powerhouse that excels in AI, cloud and digital, NiCE is consistently recognized as the market leader in its domains, with over 8,500 employees across 30+ countries.
NiCE is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, age, sex, marital status, ancestry, neurotype, physical or mental disability, veteran status, gender identity, sexual orientation or any other category protected by law.
Ready to apply?
Apply to NICE
•Are you curious, excited by experimentation and always looking to innovate?
•Do you want to work in embedded payments where you can keep learning and developing whilst getting hands-on experience?
•Do you want to have the opportunity to play an important role in a rapidly growing and exciting Fintech business?
If so, we would love to connect and collaborate!
About us
At Modulr, our vision is a world where all businesses are powered by embedded payments. Modulr enables businesses, from SMEs to Enterprise, initially across the UK and Europe to efficiently pay-in, collect and disburse funds instantly via a range of payment schemes, accounts, and card products.
We have created an industry-leading API platform with comprehensive online tools and access, to meet the demands of daily business payments. We have two routes to market. Our Core Business Payments product allows customers in any sector to connect to us and our expanding network of accounting and payroll platforms, including Sage, Xero, BrightPay and IRIS to automate payments. Our Vertical Solutions targets a growing range of industry verticals which directly connect their IT platforms to our APIs and webhooks. We solve complex payment problems for hundreds of clients in a range of industries, including Travel, Lending, Wage Advance, and Investment & Wealth.
We are deeply integrated into the payment eco-system. In the UK, we are direct participants of Faster Payments and Bacs. Modulr hold settlement accounts at the Bank of England. Our payment network connectivity includes CHAPS, Open Banking, SEPA, SWIFT and account issuance in multiple currencies. We are principal issuing members of Visa and Mastercard schemes across UK and Europe. Our regulatory permissions and governance structure are the foundations of our business. We are regulated and supervised as an Authorised Electronic Money Institution (AEMI) in the UK by the Financial Conduct Authority and in the Netherlands by De Nederlandsche Bank.
Our founding team has a wealth of experience in the payments industry and growing successful businesses. Modulr is backed by the venture arms of payments giants PayPal and FIS, as well as growth investors Blenheim Chalcot, General Atlantic, Frog Capital and Highland Europe.
Modulr now has over 400 employees spread globally across offices in London, Edinburgh, Amsterdam, and Mumbai.
Modulr values
•Building the extraordinary; going that extra mile.
•Owning the opportunity; be passionate and proud of the time you invest.
•Move at pace; reach goals faster whilst supported on your career journey.
•Achieve it together, working collaboratively and being a Modulite.
About the Role:
We are looking for a passionate Data Scientist with 2–3 years of experience to build ML/DL and Generative AI solutions for high-impact financial use cases.
Key Responsibilities:
• Develop ML/DL models for fraud detection, risk scoring, and personalization
• Build and deploy GenAI applications using LLMs
• Design and implement Agentic AI workflows
• Work with LangChain and LangGraph for orchestration
• Use tools like Claude, Cursor/CoPilot for AI-assisted development
• Implement monitoring using LangSmith
• Build APIs for model deployment
• Collaborate with cross-functional teams
• Maintain MLOps pipelines and CI/CD workflows
Key Requirements:
• 2–3 years experience in Data Science / ML / DL
• Strong Python skills
• Experience with ML frameworks (PyTorch, TensorFlow, XGBoost)
• Hands-on with GenAI, LLMs, prompt engineering
• Experience with LangChain, LangGraph
• Familiarity with Agentic AI concepts
• Exposure to tools like Claude, Cursor/CoPilot
• Experience with LangSmith for debugging/monitoring
• Knowledge of APIs, FastAPI/Flask, Docker
• Experience with MLOps tools (MLflow, DVC)
• FinTech domain experience preferred
ModInclusion
We believe that by seeing Modulr, and the world, from all sorts of angles, we can make life better for all. We want you to know that the things that make you, you — like your identity, age, ability, and background — are things that we will always celebrate and support with open arms. As such, we are keen to maximise the diversity of our workforce and actively encourage applications from anyone and everyone.
Ready to apply?
Apply to Modulr
Artefact is a new generation of data service providers specialising in data consulting and data-driven digital marketing. It is dedicated to transforming data into business impact across the entire value chain of organisations. We are proud to say we’re enjoying skyrocketing growth.
The backbone of our consulting missions, today our Data consulting team has more than 400 consultants covering all Artefact's offers (and more): data marketing, data governance, strategy consulting, product owner…
What you will be doing?
As a Data Scientist, your primary role revolves around leveraging Python and SQL to conduct data science modelling, employing statistical and machine learning algorithms to derive actionable insights. Your responsibilities will encompass:
- Data Modeling: Developing and implementing sophisticated data science models to extract valuable insights from complex datasets.
- Algorithm Implementation: Applying statistical and machine learning algorithms to solve business problems and enhance decision-making processes.
- Visualization: Utilizing visualization tools like Tableau and PowerBI to present findings in a compelling and understandable manner.
- Cloud-Based Deployment: Deploying models on cloud platforms such as MS Azure, GCP, and AWS, optimizing their functionality and scalability.
What we are looking for?
- Proficiency in Python, SQL, data science modeling, and statistical analysis.
- Strong grasp of ML algorithms and their implementation in real-world scenarios.
- Experience in visualization tools like Tableau and PowerBI, along with knowledge of deploying models on cloud platforms (MS Azure, GCP, AWS).
- Proven problem-solving skills and a solution-oriented mindset.
- Excellent communication skills to collaborate effectively within teams and with stakeholders.
- Strong business acumen with an interest in business-facing roles.
- Adaptability and a start-up mentality to thrive in a dynamic environment.
- Candidates with similar skill sets and experiences have excelled in technology firms or consultancy firms.
- Successful candidates often possess Computer Science, Electronics and Communication Engineering degrees.
Ready to apply?
Apply to Artefact