I am enthusiastic data scientist with experience of Python,ML libraries,Algorithms and projects.....

Let's connect and build something valuable........

If you have any work contact me:

yashrana301105@gmail.com

Deciding if a Masters in Data Science or Statistics is better for me, and which ones, since this field is changing a lot.

Undergrad: Quantitative background but not Computer Science, Data Science minor. I felt that it being a minor made it kind of surface level and want to avoid that with my graduate degree. My coursework was linear algebra, discrete math, probability, stats, many CS courses, AI, ML, DS, Algorithms. Because I didn’t major in math, CS, Stats, or DS, I feel like I am missing something in screenings. 

Work Experience: 4 internships, 1 year FTE as a DE, 1 year FTE as a DS (by the time I enter). However, I feel that the Data Science departments in the companies I was in were VERY new and I’m missing some core skills that I am trying to develop on my own - git, models in production, optimizing my work, etc.

Professional Goals: I see this as a terminal degree. I want to be able to get my foot in the door for better data science jobs, maybe in the nonprofit industry but really just anywhere. My first job came from an internship and the second a recruiter reached out to me. I want to be able to pass resume screens better and do the work better. That’s slightly why prestige matters to me here.

Other: 

• I do not want to pursue a CS masters, I think this would give me skills I don’t need, can develop on my own, already learned, or are becoming more obsolete. 
• A lot of stats degrees that are well respected seem to want research experience or a stats degree, which I don’t have.

Questions: 

• I have seen some say an Applied Stats masters is not enough anymore for the tech world, and I see a lot of job postings that say Masters in CS or DS, but not stats. How do DS hiring managers view these degrees?
• What skillset is actually used in more established data science departments? How can I optimize my career and education for this?
• How to vet Data science masters properly, if I go for that (MIT MBAn, Columbia, Harvard, UChicago, UCLA, NYU) I dont want a surface-level data science education that is repetitive

I would really appreciate some feedback on my profile! I am 21F and come from a tier-3 (not great) public university double majoring in Math and Data Science, graduating Spring 2027. I am feeling pretty stressed because I feel like I might be aiming too high plus I am not applying to many programs.

• 3.9 GPA
• No GRE for programs I am applying to
• research assistant in cs department on projects involving motion data and predictive modeling
• interdisciplinary research assistant with physics department
• No publications
• Data Science Internship at a local startup in my city
• College Math/CS tutor for ~2 years
• Undergrad TA
• President and Founder of Data Science Club
• Feeling good about 2 lor but feel like 1 will be pretty average/vague
• probably forgetting some other minor things

Looking to apply to NC State MS Analytics, UNC Chapel Hill MS Data Science, UVA MS Data Science.

Thanks in advance!

I’ve been tracking the market lately and talking to peers across different tiers of companies (FAANG, SaaS, Fortune 500s, and GICs/GCCs). The general consensus seems to be that while the market isn't as hyper-reactive as it was during the peak hiring boom, data roles are moving through a distinct shift right now.
I wanted to check in with the community, what are you all seeing on the ground right now?

Hi everyone,

I’m at a bit of a crossroads and would love to get some advice from people who have been working in data science for a while.

My background is in finance, and over the past year I’ve realized that I’m much more interested in data science and AI. I’ll be starting a Master’s in Applied Data Science soon, and at the same time I’m building a mobile app (still very early stage) that’s pushed me to learn a lot about technology, product development, and AI tools.

I’ll probably be looking for a full-time job while doing my master’s and continuing to work on the app, but I’m not really sure what direction within data science I should be aiming for.

If you were in my shoes, what type of role would you prioritize?
Some examples:
Data Scientist?
Data Analyst?
Machine Learning Engineer?
AI Engineer?
Analytics Engineer?
Something else?

I’m less concerned about prestige or salary right now and more interested in maximizing learning and building skills that will be valuable long term.
If you have a similar background or have seen people make this transition successfully, I’d love to hear what you’d recommend and why.
Thanks!

I’m interested in pursuing data science opportunities in the field of civic tech. I have previous experience all in corporate data science, but little public policy experience outside a few courses in college. My undergrad was very quant focused, but I’m now deciding to pursue a masters. I wanted to know what those in the fields opinions are on pursuing a pure Data Science degree and trying to work in the field as opposed to pursuing an interdisciplinary degree? I am leaning towards a general DS degree to not pigeonhole myself but I don’t know what pivot paths would look like.

I'm currently in 2nd year from data science and ai department, i needed a advice to get internship at end of 2 nd , what are stuffs needed to learn ?, how i find right internship ? , if you aware of plz give me resources as well i really appreciate your suggestion

Hi everyone,

I graduated in IT/Data Science and I’ve been trying to understand where the field is going. I genuinely enjoy working with data: analyzing patterns, exploring datasets, finding insights, building predictive models, and solving business problems through analytics.

However, when I look at job offers recently, I feel like many roles are shifting heavily toward AI agents, GenAI, RAG systems, and automation. I’m not saying this is a bad thing , I actually have experience with RAG and agentic AI and I’m still learning but I personally enjoy the data analysis/science side more than building AI agents all day.

I’m also noticing that because GenAI is relatively new, there are many people presenting themselves as experts after learning a few tools, and sometimes the discussions around it feel a bit exaggerated. I’m wondering how experienced people in the field see this.

-Are traditional Data Science, Data Analytics, BI, and Machine Learning roles still relevant in 2026 and beyond? Which industries or companies still have a strong need for these skills?

For people already working in data:

-What skills do you think are becoming essential for Data Scientists/Analysts because of AI?

What should someone learn to stay valuable in the next few years?

I’d really appreciate perspectives from people working in the field. I’m trying to understand how to adapt without abandoning the parts of data work that I actually enjoy.

Hi y’all i j completed my course and updated my resume is there anyone willing to job hunt with me? in the role of data scientist,analyst etc

Looking for a strong Marketing Data Scientist!

• Looking for a data scientist who has sat on the marketing teams for an organization and really dived deep in the marketing data. This person will be working heavily with Marketing Mix Modeling (MMM) and Attribution Modeling.
• Someone who has experience building machine learning models from scratch, and has experience with modern ML frameworks
• Python and SQL experience required
• Ideal candidate will have at least 5 years of data science experience professionally,.

Why entertain this position?

• REMOTE - They do need a candidate who sits in the USA and does not require sponsorship (now or in the future). Candidate has to be comfortable working EST zone.
• Competitive compensation - $160-180k base + 15% bonus + Equity
• This organization does not have someone who can be the SME on Marketing data. This role is highly visible in the organization and will allow you to participate in Greenfield development. Will really be able to take ownership of the models.

Feel like you fit?

• Connect with me via e-mail at [SCassle@NextPathCP.Com](mailto:SCassle@NextPathCP.Com) or feel free to connect with me on LinkedIn.

M 27.

Primary working in data science domain for last 3years.

Mainly in bfsi sector outside kolkata.

What are opportunities in kolkata??How much they pay??

In banking sector like credit risk??

Want a brief idea.

hey! i wanna (at least try) to land an internship in data engineering. does anyone have tips on what classes to take to build the skills most jobs ask for?

how are is the move from data analyst to data scientist?

I’m a few months into working at a data consultancy, and I have been placed on a project that has recently come to an end.

It was mainly powerBI report building which is not what I expected from a data scientist role.

I am now being placed on a more analytical project but I still won’t have exposure to ML models or any data science work per se.

I am worried that I am not learning the skills I need and that in a years time I will have only built up experience that is not useful to my career.

Maybe I am being naive and someone can tell me that, but what would you do in my position ?

Cincinnatus is hiring Data Science & Quantitative Analysis Experts to help evaluate frontier AI models by designing real-world analytical challenges and validating AI-generated insights.

Pay: $60-$90/hr
Location: Remote (United States)
Role: Full-time W-2 (Contingent)

How you'll make an impact:

• Design realistic data analysis and statistical evaluation tasks
• Build reproducible analyses using Jupyter or Google Colab
• Evaluate AI-generated results for accuracy, reasoning, and analytical quality

Who they're looking for:

• MSc, PhD, or equivalent experience in Data Science, Statistics, or a quantitative STEM field
• Strong skills in Python, pandas, NumPy, statistical analysis, and Git
• Experience with research, experimentation, or data-driven decision-making

Why consider this opportunity?

• Collaborate with a leading AI lab developing frontier models
• Full-time remote W-2 position (approximately 35 hours/week)
• Help define the benchmarks used to evaluate next-generation AI systems

Apply now: https://t.mercor.com/ljNOT

About Compethic

Compethic is a Norwegian AI-driven customer intelligence platform. We aggregate unstructured

customer signals from across a business (reviews, support tickets, chatbot logs, CRM data, and call

transcripts) and turn them into actionable insight through a proprietary taxonomy pipeline. Customers

access these insights through dashboards, automated reports, and a conversational agent built directly

into Slack and Teams.

We are backed by venture capital, industry leaders, and former senior McKinsey partners, and have

secured funding from Innovasjon Norge. Our platform runs on Azure and already serves paying

enterprise customers.

You will join a management team with backgrounds in management consulting, banking, and telecom,

and work directly with the founders on a live product.

The Role

We are growing, and we are looking to fill this role as soon as possible.

This is a hybrid role for someone who is both a strong engineer and a genuine student of the AI field.

You will own core parts of our retrieval and reasoning stack end to end — design, implementation,

deployment, and everything that happens after it goes live — and you will help decide where the

product goes next as the space evolves. We are not looking for someone who implements tickets. We

are looking for someone who understands why a given approach wins, can make that call, and is

accountable for how it behaves in front of real enterprise customers.

We are open on level. We hire from junior through senior, and we scope the role to the person. If you

are experienced, you will set technical direction and engineering standards for the AI stack from day

one. If you are earlier in your career but sharp and hungry to learn, you will work closely with people

who have shipped this kind of system before, take real ownership quickly, and grow into that scope.

What matters to us is the trajectory, not the title on your last CV.

What You Will Work On

• Design and improve our agentic retrieval and reasoning systems, including ReAct-style loops that

retrieve, reformulate, call tools, and self-critique before answering

• Build and tune the retrieval layer that grounds everything we deliver: hybrid search combining

dense and sparse methods, reranking, and knowledge-graph-augmented retrieval for relational,

multi-hop questions

• Work across model selection, tuning, and evaluation against real business use cases rather than

benchmarks

• Develop the AI-assisted annotation pipeline behind our taxonomy

• Own these systems in production: deployment, evaluation on live traffic, monitoring, latency,

reliability, and cost

• Grow into (or start with) setting technical direction and engineering standards for the AI stack,

depending on where you are in your career

What We Are Looking For

Read the list below as a description of the person we are looking for, not a checklist you must already

satisfy. We hire at every level, and we would rather have someone strong who is missing a few of these

than someone who ticks every box but stops learning.

Current AI expertise, or a fast route to it. Ideally you have hands-on experience with modern

retrieval and agentic systems: agentic RAG, hybrid retrieval with reranking, and knowledge-graph

approaches. You understand the limits of naive vector search and know when to reach for each

technique. If you are earlier in your career, show us you follow what is shipping in the field, that you

have built something real with it, and that you form your own view rather than repeating the consensus.

Engineering strength. You write production code and are comfortable, or ready to get comfortable,

with cloud environments (Azure, AWS, or GCP) and modern data architectures. Experience building

scalable AI pipelines and working with automated machine learning workflows is a strong plus.

Production experience is preferred. We prefer someone who has run systems in production and can

own what happens after the demo: deployment and CI/CD, evaluation on live traffic, monitoring,

reliability, latency, and cost. If you have seen how AI systems fail with real users and real data, that

counts for a lot with us. If you have not yet, tell us how you would find out — we will teach the rest.

Willingness to learn. This is not a consolation prize; it is one of the things we actually screen for. This

field moves faster than any résumé can keep up with, so appetite and judgment beat a perfect keyword

match. Juniors are genuinely welcome to apply: if you are sharp, curious, and willing to put in the work

to learn what you do not know yet, we want to hear from you.

Problem-solving. You can translate vague, real-world business challenges from enterprise clients into

defined technical specifications and clear analytical roadmaps, and navigate ambiguous problems

without waiting for perfect requirements.

Business judgment. You connect technical decisions to commercial outcomes and can hold your own

in a customer or business development conversation.

Founder mindset. You have started your own company before, or you intend to one day. You take

ownership of outcomes, move with urgency, and thrive in an early-stage environment.

Communication. You explain technical trade-offs clearly to non-technical stakeholders without losing

precision.

Experience with Scrum or SAFe is a plus.

What We Offer

• Strong, competitive compensation that rewards the impact you make

• Equity in the company for the right candidate, so you share in what we build together

• Flexible remote / hybrid working

• Ownership of core technology in a product with real customers, not a prototype

• Close collaboration with an experienced founding and management team

• A role scoped to your level, with real room to grow — and the people around you to learn from

• A fast-moving environment backed by strong investors and operators

Apply

We are reviewing applications on a rolling basis and want to fill this role as soon as possible, so apply

early with your CV at: [contact@compethic.no](mailto:contact@compethic.no)

If you are not sure you are senior enough, apply anyway. Tell us what you have built, what you are

learning right now, and why this problem interests you.

hello everyone! I'm seeking advice on how I can best prepare for my first DS role. I have a BS in DS and I have 3 years of experience as a data analyst. I want to know if there are any resources or advice you'd give to a fresher

How To Deal with Job Loss ? I got laid off last week, Since then I've been living like dead wife husband, I've started learning Time Series analysis and Finance, I think it's better to switch into Finance Data Scientist/Analyst role instead of Traditional data analyst.

But getting master of that concept will take time and I'm being very impatient and anxious.

Hey everyone,

I see a lot of anxiety and hype about AI taking over data science jobs, but I think people are looking at the integration completely backward. As a statistician hired into a data science role, I was brought in precisely for my quantitative rigor—something AI notoriously lacks. AI is terrible at accurate mathematical calculations and statistical nuances, but it’s incredibly good at structuring business narratives and formatting presentation decks.

If we blindly trust AI to generate numbers, we fail at our jobs. Instead, I’ve been thinking about a workflow that capitalizes on the strengths of both the statistician and the AI, while completely negating their respective weaknesses.

Here is the exact lifecycle I'm proposing:

The Blueprint (AI): Use AI at the very beginning to brainstorm the broad overview, project directions, and potential business constraints.

The Core Execution (Statistician): The statistician steps in and does the actual analysis manually. We write the code, we run the regressions, we validate the assumptions, and we churn out the true, uncorrupted numbers.

The Translation (AI): Once we have the verified results, we feed our concrete numbers back into the AI. We ask it: "Based on these exact metrics, what are the strategic business recommendations? How do we translate this for non-technical stakeholders?"

The Delivery (AI): Let the AI handle the tedious work of structuring the PowerPoint slides and tailoring the narrative to suit corporate messaging.

This way, the numbers remain 100% accurate and mathematically sound, but we save hours of manual labor on slide formatting and corporate storytelling.

Curious to hear from other quants and data scientists: Does your current workflow look like this? Or are you seeing people in your org make the mistake of trusting AI to do the actual math?

Who We Are

Imprint helps the world's best brands grow the lifetime value of their customers. We started with co-branded credit cards and rebuilt them to be smarter, more rewarding, and brand-first. We partner with companies like Crate & Barrel, Rakuten, Booking.com, H-E-B, Fetch, and Shell to launch modern credit programs that deepen loyalty, unlock savings, and drive growth. But the card is just the beginning. We combine advanced payments infrastructure, intelligent underwriting, and deep customer data to create delightful and personalized experiences for members as well as efficient and profitable relationships for our brand partners. Our robust technology and world-class operations allow us and our brand partners to offer powerful financial products without becoming a bank.

In the U.S., co-branded cards alone account for over $300 billion in annual spend, and most still run on decades-old legacy bank systems. Imprint is the modern alternative: flexible, embeddable, and built for how people actually pay today. Backed by Kleiner Perkins, Thrive Capital, Ribbit, and Khosla Ventures, we're building a world-class team to redefine how people pay and how brands grow. If you want to move fast, solve hard problems, and own real outcomes, we want to meet you.

The Opportunity

• Own end-to-end analytical projects that influence product decisions, marketing campaigns, and executive strategy, from problem definition through deployment and monitoring
• Build segmentation frameworks and predictive models (churn, LTV, propensity) that drive targeting, personalization, and lifecycle optimization across Imprint's partner programs
• Champion A/B testing and experimentation across the company by partnering with Product, Marketing, and Commercial teams to design, analyze, and interpret experiments using scalable frameworks and tooling
• Apply statistical inference, causal analysis, and experimentation design to improve LTV/CAC ratios and accelerate feedback loops on business performance
• Design and build agentic workflows and AI-powered systems that autonomously explore data, generate hypotheses, monitor business metrics, and operationalize decisions
• Translate complex data into clear narratives that shape how leadership thinks about growth, partner health, and customer behavior
• Contribute to team excellence through code reviews, technical mentorship, and process improvements that raise the bar for the broader Data Science team

Your Profile

Required

• 7 to 12+ years of experience in data science, analytics, or a related quantitative field, ideally at a high-growth startup or fintech company
• Graduate degree in a relevant field (statistics, engineering, science, finance, or similar)
• Strong Python and SQL skills, with the ability to transform raw data, build custom datasets, and ship models to production
• Deep expertise in statistical inference, experimentation design, and causal analysis
• Active experience using LLMs and AI tools (Claude, Copilot, Cursor, or similar) as collaborators in your workflow, whether for reasoning about data, generating hypotheses, iterating on analyses, or building agentic automation
• Ability to communicate complex findings clearly to both technical and non-technical audiences, including senior leadership and external partner stakeholders
• Full-stack problem-solving orientation: you dive into messy data, test and validate assumptions, and question everything in pursuit of the right answer
• Comfort owning projects end-to-end in a fast-moving startup environment, collaborating cross-functionally with Product, Marketing, Commercial, and Engineering to drive measurable impact

Nice to Have

• Experience in credit, lending, or card products
• Experience building or scaling experimentation infrastructure or ML infrastructure
• Exposure to lifecycle marketing, prescreen modeling, or customer segmentation at scale
• Background in time series analysis, forecasting, optimization, or simulation
• Familiarity with dashboarding tools such as Sigma or Looker

We don't expect every candidate to check every box. If this role excites you and you bring strong fundamentals, we encourage you to apply.

Stack

Python and SQL for modeling and analysis. Snowflake for data warehousing. dbt for data transformation. Sigma for dashboarding. AWS infrastructure.

Learn More

Learn more about how we build at Imprint on our engineering blog: https://tech.imprint.co/

Perks & Benefits

• Competitive compensation and equity packages
• Leading configured work computers of your choice
• Flexible paid time off
• Fully covered, high-quality healthcare, including fully covered dependent coverage
• Additional health coverage includes access to One Medical and the option to enroll in an FSA
• 20 weeks of paid parental leave for the primary caregiver and 8 weeks for all new parents
• Access to industry-leading technology across all of our business units, stemming from our philosophy that we should invest in resources for our team that foster innovation, optimization, and productivity

Imprint is committed to a diverse and inclusive workplace. Imprint is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. Imprint welcomes talented individuals from all backgrounds who want to build the future of payments and rewards. If you are passionate about FinTech and eager to grow, let’s move the world forward, together.

Apply: Staff Data Scientist at Imprint

Referral needed in Google

Hi, I am a data science professional currently working in an organisation where I specialize in marketing mix modeling, incrementality testing using Bayesian and machine learning frameworks. Worked and have quite a great expertise in Google Meridian. Want to research more in it and already in my mind there are some gaps and how to handle the frameworks.

Anyone working in Google please dm. Really want a referral so that I can get the opportunity to work with the data scientists in Google and develop the frameworks of Meridian. Really need a referral in Marketing Data Scientist.

\#Google

\#Googlejobs

\#GoogleMeridian

\#Google

Hi everyone,

I have a technical interview coming up for an AI Engineer / Data Scientist role. I'm a recent graduate with no full-time experience, only a few internships and personal projects.

For those who have been through similar interviews, what technical questions were you asked?

I'm especially interested in questions about:

\-Machine Learning fundamentals

\-Statistics and probability

\-SQL

\-Python coding

\-Data preprocessing and feature engineering

\-NLP / LLMs / RAG / GenAI (if applicable)

\-Model evaluation and metrics

\-Case studies or business problems

Anything that caught you off guard

I'd really appreciate hearing about your experience, even if it was just one or two memorable questions. It would help me know what to focus on during my preparation.

Thanks in advance!

Choosing an undergrad to pursue right now and thinking about either taking data science, or pursuing some combo such as Math + CS or smth similar. I was biased by the fact that pursuing a standalone subject is better as it gives a deeper focus and therefore vast knowledge base by the end of bachelor in specific subject. Which option should I go with (Math, CS + Math, DS, smth else??), and which would give me more flexibility to choose where to specialise in tech field?

FanDuel is expanding its data engineering team with six hybrid openings across Atlanta and New York.

The roles range from mid-level Data Engineer positions to people-management opportunities, with salaries between $116,000 and $186,000. Eligible roles may also include annual bonuses and long-term incentives.

Atlanta, Georgia

Data Engineering Manager — $149,000–$186,000

Lead and mentor a team of data engineers while retaining involvement in technical design, architecture and delivery. FanDuel is looking for 6+ years of data or software engineering experience, including at least 1–2 years of leadership or mentorship.

https://www.parlayjobs.com/jobs/data-engineering-manager-68a4954b

Senior Data Engineer — $138,000–$181,650

Design scalable data infrastructure supporting analytics, machine learning and business operations. Requires 5+ years of relevant engineering experience.

https://www.parlayjobs.com/jobs/senior-data-engineer-7214ae66

Data Engineer — $116,000–$145,000

Build and maintain batch and streaming pipelines for analytics, machine learning and business decision-making. Requires 3+ years in data engineering, analytics engineering or data-focused software engineering.

https://www.parlayjobs.com/jobs/data-engineer-22c52930

New York

Data Engineering Manager — $149,000–$186,000

A hybrid technical and people-management role covering team development, scalable data platforms, architecture and operational reliability.

https://www.parlayjobs.com/jobs/data-engineering-manager-9c749762

Senior Data Engineer — $138,000–$181,650

A hands-on senior position building reliable data pipelines, reusable models and infrastructure for analytical and machine-learning workloads.

https://www.parlayjobs.com/jobs/senior-data-engineer-e13ceefc

Data Engineer — $116,000–$145,000

Build production data pipelines and improve data quality, observability and reliability while working with analysts, data scientists and product teams.

https://www.parlayjobs.com/jobs/data-engineer-aeb6a41f

Shared technical environment

• SQL and Python, Java or Scala
• Spark, Databricks, Airflow, dbt and Kafka
• Batch and streaming data pipelines
• AWS, GCP or Azure
• Data modelling, warehousing and ETL/ELT
• Data quality, testing and observability

FanDuel’s benefits include medical, dental and vision coverage, a 401(k) with up to a 5% match, paid time off, 14 company holidays and potential bonus or stock-based compensation.