Two offers, Noida and Gurugram: a side-by-side comparison worksheet

Why CTC is the wrong first number

A CTC figure bundles items that never reach your bank account monthly: employer PF contribution, gratuity provision, insurance premiums, and variable pay that depends on targets. Two offers with the same CTC can differ a lot in take-home, and a lower-CTC offer can leave you with more money each month. Before you compare anything, ask each HR team for a monthly salary breakup and the expected take-home under the tax regime you plan to use.

Also note which state each office is in. Noida is in Uttar Pradesh and Gurugram in Haryana, and state-level deductions on a payslip can differ. This payslip guide explains what to look for rather than assuming.

The worksheet

Fill one column per offer. The "Source" column reminds you where each number must come from, so you do not fill it with a guess.

Lines 1–7 are arithmetic; lines 8–10 are judgement. Do the arithmetic first so it does not bias the judgement.

Getting honest commute numbers

The commute is where Noida-versus-Gurugram comparisons usually go wrong, because people check the route on a weekend afternoon.

• Check at your actual start time. Open a journey planner at the hour you would leave, on a weekday.
• Count interchanges. East Delhi to Gurugram's Cyber City typically means Blue or Pink Line, then Yellow Line, then Rapid Metro from Sikanderpur. Each change adds walking and waiting.
• Include the last kilometre. An office "near" Noida Sector 62 or Cyber City may still be a 15-minute walk from the station.
• Multiply by real office days. A three-day hybrid policy means about 13 round trips a month, not 22.

A script to run the numbers

The values below are hypothetical placeholders. They do not describe any real offer, rent or fare. Replace every one.

# Hypothetical inputs only. Replace each value with your own figures.
offers = {
    "A (Noida)": {
        "take_home_fixed": 52000,     # ₹ per month, from HR breakup
        "variable_expected": 2000,    # ₹ per month, your realistic estimate
        "rent_and_utilities": 15000,  # ₹ per month, your own search
        "round_trip_cost": 120,       # ₹ per office day
        "one_way_minutes": 35,
        "office_days_per_week": 5,
    },
    "B (Gurugram)": {
        "take_home_fixed": 58000,
        "variable_expected": 3000,
        "rent_and_utilities": 21000,
        "round_trip_cost": 90,
        "one_way_minutes": 70,
        "office_days_per_week": 3,
    },
}

WEEKS_PER_MONTH = 52 / 12

def monthly(o):
    days = o["office_days_per_week"] * WEEKS_PER_MONTH
    commute_cost = days * o["round_trip_cost"]
    commute_hours = days * o["one_way_minutes"] * 2 / 60
    surplus = (o["take_home_fixed"] + o["variable_expected"]
               - o["rent_and_utilities"] - commute_cost)
    return round(surplus), round(commute_hours, 1), round(days, 1)

results = {name: monthly(o) for name, o in offers.items()}
for name, (surplus, hours, days) in results.items():
    print(f"{name:<14} surplus ₹{surplus:>7,}/month   commute {hours:>5} h over {days} office days")

(a_s, a_h, _), (b_s, b_h, _) = results.values()
extra_money = b_s - a_s
extra_hours = b_h - a_h
print(f"\nB leaves ₹{extra_money:,} more per month and costs {extra_hours:+.1f} commute hours.")
if extra_hours > 0:
    print(f"That is about ₹{extra_money / extra_hours:,.0f} per extra hour spent commuting.")
elif extra_money >= 0:
    print("B is ahead on both money and time with these inputs.")

With these placeholder numbers, Offer B leaves about ₹2,430 more each month and costs about 5 more commute hours, even though its one-way trip is twice as long, because it needs fewer office days. Your inputs may reverse that. The script's job is to make the trade-off explicit as rupees per extra commute hour, so you can decide whether that rate is worth it to you.

Reading the result

A few patterns come up repeatedly when people fill this in:

• Office days often matter more than distance. A longer commute three days a week can cost less time than a shorter one five days a week.
• Rent can wipe out a higher salary. If living near one office costs much more, compute the surplus for living farther away as well and compare both.
• Variable pay is uncertain. Run the script twice, once with variable pay at zero. If the decision flips, you are betting on the variable.
• Hours have uses. Five hours a month is two evenings of study, or a good start on a small portfolio project. If you plan to upskill, time is part of the value of the offer.

When the numbers are close

If the surplus difference is small, decide on line 10: the work. Ask each hiring manager what data you would touch in your first month and who would review your first analysis. The role where you learn SQL, dashboards and stakeholder communication faster is usually the better offer over two years, whichever side of the Yamuna it sits on.

For more on reading pay structure, see our guide to data scientist salaries in India, which explains what published salary data does and does not measure. And if neither offer is quite the role you want, the Data Analyst programme takes 3–4 months, and because classes are live online and recorded, it can run alongside whichever job you take.